load("data_combined.RData")
df$`Device Type` <- relevel(df$`Device Type`, ref = "HMII")
bc <- 14:42
cyt <- 43:80
bcellcyto <- c(bc,cyt)
groups <- make.names(c("AgeGreater60", 
                       "Sex",
                       "LowIntermacs",
                       "RVAD", 
                       "Sensitized",
                       "VAD Indication", 
                       "Survival"
                       ))
FDRcutoff <- 0.1
pcutoff <- 0.05

# HMII wide
df.HMII <- droplevels(subset(df, df$`Device Type` == "HMII"))

suppressMessages(require(knitr, quietly = T))
suppressMessages(require(kableExtra, quietly = T))
suppressMessages(require(rmarkdown, quietly = T))
suppressMessages(require(plyr, quietly = T))
suppressMessages(require(dplyr, quietly = T))
suppressMessages(require(parallel, quietly = TRUE))
suppressMessages(require(ggsci, quietly = TRUE))
suppressMessages(require(ggplot2, quietly = T))
suppressMessages(require(GGally, quietly = T))
suppressMessages(require(reshape2, quietly = T))
suppressMessages(require(stringr, quietly = T))
#capture.output(invisible(suppressMessages(require(WGCNA, quietly = T))))
suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
suppressMessages(require(fdrtool, quietly = TRUE))
suppressMessages(require(pheatmap, quietly = TRUE))
suppressMessages(require(RColorBrewer, quietly = TRUE))

# Efron's double standardization
double_standardize <- function(x, niter = 1000) {
    for(i in 1:niter) x <- t(scale(t(scale(x))))
    return(as.data.frame(x))
}

# Error bars
invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

HeartMate-II analysis

Introduction

20 heart-failure patients receiving the Heartmate-II were sampled at 7 timepoints after MCS device implantation. Each sample consisted of 67 biomarker measurements – 29 B-cell markers and 38 cytokine markers. Additionally, each patient was associated with 7 categorical variables, such as age, sex, interMACS score, and survival. However, due to practical limitations, not all samples were complete. After accounting for missing data, there are a total of 105 datapoints.

Specific aims

  • Our aim was to identify if any of the 67 biomarkers are associated with any of the 7 categorical variables, at a false discovery rate of 10%.

Methods

Raw data

Data was collected at a single university medical center.

missing.ix <- Reduce(intersect, apply(df.HMII[,bcellcyto], 2, function(x) which(is.na(x))))
df.raw <- df.HMII[-missing.ix,]
df.raw <- df.raw[order(df.raw$PatientID),]
rownames(df.raw) <- 1:nrow(df.raw)
kable(df.raw, 
      digits = 3,
      row.names = T,
      caption = "HeartMate-II Raw Data"
) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%", height = "300px")
HeartMate-II Raw Data
PatientID Time Age AgeGreater60 Sex LowIntermacs InterMACS RVAD Sensitized VAD Indication Device Type Outcome Survival num Total PBMC num lymph lymph live lymph CD3 of live lymph CD19 of live lymph CD19+CD27- CD19+CD27+ CD27+38++plasma blasts CD27-38++ transitional CD27-IgD+ mature naive CD27+IgD- switched memory CD27-IgD- switched memory CD27+IgD+ unswitched memory CD27+IgD-IgM+ switched memory CD27+IgD+IgM+ nonswitched memory CD19+27+IgG+IgM- memory CD19+24dim38dim naive mature CD19+24+38++transitional CD19CD24hiCD38-memory CD19+27-38+CD5+transitionals CD19+CD268+ CD268 of +27-38++transitional CD19+CD11b+ CD19+CD5+ CD19+CD27+CD24hi CD19+CD5+CD24hi CD19+CD5+CD11b+ CD19+27+IgD-38++IgG ASC IL-12(p40) IL-12(p70) IFN-g TNF-a TNF-b IL-4 IL-5 IL-9 IL-10 IL-13 IL-17A IL-1a IL-1b IL-2 IL-3 IL-6 IL-15 TGF-a IFN-a2 IL-8 GRO Eotaxin MDC IP-10 MCP-1 MCP-3 Fractalkine MIP-1a MIP-1b GM-CSF IL-7 G-CSF VEGF EGF FGF-2 Flt-3L IL-1RA sCD40L
1 1 0 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 169154 35496 20.98 99.53 25.37 19.82 86.02 13.98 1.26 2.54 57.81 11.74 28.06 2.39 21.74 14.36 0.72 81.05 2.86 13.60 0.58 95.22 84.83 10.47 4.33 8.50 1.39 3.13 1.09 1.71 2.16 124.000 20.926 2.72 2.500 1.010 7.273 4.617 1.760 21.20 277.000 3.483 9.878 1.160 12.334 1.090 5.71 2.18 45.38 126.00 119.000 525.00 1174 392 3.03 27.25 2.030 47.654 60.843 1.268 33.267 486.000 54.27 6.05 1.92 4.47 793.00
2 1 1 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 63082 9915 15.72 99.26 32.45 26.26 90.06 9.94 0.89 2.09 54.27 7.70 35.71 2.32 31.10 20.47 1.57 84.06 2.59 10.79 0.26 97.02 92.59 7.50 3.13 6.42 1.32 2.44 1.49 1.71 1.71 156.000 24.857 2.72 2.500 1.010 1.270 48.726 1.760 29.97 388.000 1.350 1.952 1.160 109.000 5.715 3.89 3.18 102.00 191.00 95.541 463.00 1079 552 3.03 35.53 2.030 62.089 12.293 2.005 109.000 509.000 64.48 26.13 1.92 19.50 841.00
3 1 3 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 75921 21721 28.61 99.31 24.86 33.01 86.38 13.62 1.54 1.26 45.74 10.98 40.44 2.84 31.80 18.04 1.15 86.21 4.82 6.56 0.24 90.41 73.33 10.00 7.25 10.12 3.22 5.67 2.05 32.32 14.40 259.000 28.330 14.74 12.770 3.369 5.615 28.055 7.406 54.61 438.000 3.867 8.638 3.690 57.616 12.778 5.85 56.84 121.00 193.00 186.000 510.00 1365 464 35.08 152.00 2.030 87.775 44.032 10.100 81.038 687.000 70.06 117.00 1.92 151.00 801.00
4 1 5 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1.71 5.07 134.000 41.230 2.72 2.813 1.809 4.930 23.012 1.760 26.38 316.000 1.219 1.230 2.735 15.579 5.021 2.73 25.80 78.31 231.00 213.000 462.00 2053 583 4.14 104.00 2.030 55.375 49.222 6.820 29.004 414.000 61.03 43.25 1.92 64.51 1230.00
5 1 8 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 213808 36002 16.84 99.19 35.95 14.55 67.94 32.06 4.06 2.17 27.92 28.92 39.64 3.52 20.31 9.10 1.33 68.69 7.10 20.42 1.88 87.38 44.25 10.78 11.12 19.15 5.54 7.35 1.07 1.71 3.83 228.000 23.577 2.81 2.500 1.666 6.792 39.103 1.760 43.65 450.000 2.412 6.904 2.197 19.163 2.445 3.75 19.72 56.79 134.00 184.000 496.00 1214 430 3.03 90.07 2.030 72.549 44.681 4.448 51.520 735.000 78.74 94.11 1.92 133.00 912.00
6 1 21 65 older Male High 2 No NA BTT HMII Alive s/p OHT alive 106970 31867 29.79 99.18 42.36 15.02 84.69 15.31 1.94 2.11 55.41 13.14 29.13 2.32 20.22 12.88 2.08 83.21 5.20 9.50 1.10 94.44 73.00 9.04 6.99 9.22 2.80 4.78 1.44 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
7 3 0 81 older Male Low 3 No NA DT HMII Died dead 119377 77509 64.93 99.61 58.50 7.99 62.67 37.33 9.10 4.82 36.73 31.90 25.62 5.74 11.47 8.95 0.40 69.54 7.69 9.41 2.61 76.44 57.91 23.29 20.76 15.39 5.42 14.90 2.60 6.39 6.65 11.372 21.497 3.51 2.320 6.303 1.590 6.039 2.111 5.18 4.727 1.920 1.450 2.558 38.074 5.729 2.57 19.65 32.02 123.00 133.000 380.00 1899 1054 26.14 112.00 2.230 32.026 20.483 7.783 58.902 70.513 7.02 95.30 0.39 89.23 624.00
8 3 1 81 older Male Low 3 No NA DT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 41.70 5.35 39.418 40.838 13.66 17.416 2.457 4.050 57.842 9.006 7.14 46.122 4.879 6.666 3.299 119.000 26.234 2.86 67.14 112.00 206.00 180.000 411.00 1271 4441 35.46 169.00 33.419 100.000 54.262 5.253 43.646 118.000 24.49 79.63 0.39 224.00 1714.00
9 3 3 81 older Male Low 3 No NA DT HMII Died dead 96940 57658 59.48 99.70 55.52 9.21 72.15 27.85 3.85 3.82 48.41 21.75 23.49 6.35 12.10 16.04 0.28 75.30 3.72 6.65 2.11 80.52 67.33 15.80 15.23 6.71 1.32 8.86 0.26 17.15 6.00 20.672 15.342 7.00 5.509 7.681 2.040 8.932 3.799 4.45 17.129 2.699 3.725 3.108 25.794 15.326 2.57 74.20 34.62 85.04 162.000 348.00 897 1361 21.65 102.00 6.821 27.057 24.672 5.253 34.257 70.513 13.56 63.96 0.39 118.00 1014.00
10 3 5 81 older Male Low 3 No NA DT HMII Died dead 154373 73969 47.92 99.65 45.18 8.77 67.36 32.64 6.59 2.77 39.10 26.28 27.95 6.67 17.89 15.71 0.39 71.57 6.26 9.88 1.78 81.86 72.07 20.12 20.17 14.80 5.99 14.65 3.71 4.28 2.29 12.657 16.490 2.31 2.320 6.816 1.590 6.845 1.520 4.74 2.120 1.920 1.450 1.980 99.474 11.334 1.58 47.29 61.82 48.64 192.000 320.00 738 1071 10.05 46.41 2.230 21.777 11.020 1.520 37.403 27.750 2.89 23.91 0.39 52.69 361.00
11 3 8 81 older Male Low 3 No NA DT HMII Died dead 155610 63375 40.73 99.51 38.42 12.40 71.69 28.31 4.36 2.62 37.82 23.43 33.50 5.24 17.37 14.92 0.60 65.04 2.80 11.23 2.54 83.55 37.56 15.55 15.45 9.55 2.66 10.91 0.28 8.54 4.12 17.955 15.342 3.51 2.320 6.816 1.590 6.439 1.605 5.62 13.939 2.314 1.782 2.042 79.869 8.070 2.42 84.63 34.26 38.88 162.000 335.00 822 1054 16.46 84.38 2.980 19.937 20.483 2.734 21.566 90.169 10.50 41.25 0.39 77.29 562.00
12 3 14 81 older Male Low 3 No NA DT HMII Died dead 244245 115109 47.13 99.47 47.40 9.37 67.50 32.50 3.99 2.08 50.08 26.01 17.22 6.68 15.34 15.25 0.23 77.86 3.21 10.49 1.48 82.13 59.19 21.45 19.77 14.45 2.57 14.94 0.94 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
13 4 1 58 younger Male Low 3 Yes No BTT HMII Alive s/p OHT alive 246811 86529 35.06 93.49 67.96 17.48 81.66 18.34 2.05 6.85 51.33 13.69 29.91 5.08 15.68 5.94 0.19 61.95 0.76 13.51 4.07 92.62 34.06 6.90 12.07 8.00 2.54 3.85 0.26 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
14 4 3 58 younger Male Low 3 Yes No BTT HMII Alive s/p OHT alive 406771 90343 22.21 97.34 49.28 17.02 75.04 24.96 4.47 18.36 28.45 21.86 46.07 3.62 24.97 4.33 0.13 47.41 1.64 19.70 18.15 72.76 8.08 25.69 28.35 13.95 4.66 19.20 0.41 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
15 4 21 58 younger Male Low 3 Yes No BTT HMII Alive s/p OHT alive 330191 81636 24.72 94.57 45.14 14.70 70.51 29.49 8.94 16.23 38.92 23.53 30.83 6.72 23.93 7.07 0.18 46.63 2.35 16.12 8.59 74.45 39.90 24.19 21.18 12.62 3.26 12.93 0.38 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
16 7 0 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 1073919 336538 31.34 100.00 63.29 14.80 93.05 6.95 0.73 8.05 75.49 4.55 19.16 0.80 7.25 8.78 28.04 88.72 2.51 5.18 1.02 90.49 68.18 2.32 4.09 1.69 3.48 1.27 5.78 103.00 34.40 73.796 17.487 330.00 74.260 18.401 16.683 51.466 82.515 12.80 150.000 15.178 15.208 9.970 26.235 21.107 8.44 133.00 39.26 415.00 111.000 298.00 443 296 138.00 125.00 10.503 37.240 39.468 13.629 111.000 409.000 77.43 78.89 10.95 301.00 5998.00
17 7 1 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 270301 97229 35.97 91.63 18.43 61.67 97.68 2.32 0.20 5.85 69.45 1.63 28.19 0.73 27.57 21.43 17.05 84.32 7.26 2.73 0.84 99.44 97.60 0.54 2.64 1.51 3.63 0.29 3.40 16.38 6.78 46.507 34.398 28.79 11.752 0.814 4.999 1481.000 8.712 8.92 17.769 2.325 4.279 2.360 63.109 16.386 5.90 34.66 152.00 295.00 55.059 278.00 1037 409 21.48 40.32 6.177 30.898 20.452 2.676 177.000 224.000 36.40 47.22 0.92 160.00 5604.00
18 7 3 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 499016 78019 15.63 89.41 25.83 51.38 92.98 7.02 0.32 4.66 51.34 6.31 41.59 0.76 36.49 5.96 22.81 73.48 6.73 7.93 0.33 98.78 96.52 1.31 2.06 5.19 2.68 0.59 2.45 16.38 6.52 31.962 18.537 34.49 16.410 2.113 4.287 78.861 9.272 9.04 17.769 2.902 2.861 3.595 11.983 22.669 9.71 14.43 38.26 183.00 56.470 219.00 307 219 29.76 47.34 5.053 3.891 7.570 2.878 33.464 191.000 16.92 81.03 0.92 99.54 4583.00
19 7 8 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 115825 32513 28.07 91.04 44.61 15.76 80.52 19.48 4.16 7.82 49.91 18.37 30.52 1.20 14.87 3.33 30.19 61.08 8.70 12.56 1.58 89.80 76.71 2.87 6.07 11.12 8.08 1.59 1.17 37.61 21.91 70.846 24.832 57.61 38.448 5.988 8.685 44.610 13.488 17.95 55.857 7.772 7.570 7.238 14.443 23.376 10.40 81.88 77.46 332.00 132.000 332.00 1453 632 38.47 104.00 12.646 40.206 31.261 7.509 79.659 354.000 26.71 180.00 0.92 274.00 7156.00
20 7 21 65 older Female High 2 No Yes BTT HMII Alive s/p OHT alive 407080 97651 23.99 91.29 66.96 12.20 84.66 15.34 2.88 5.60 61.89 13.73 22.65 1.74 20.16 7.23 29.39 68.49 5.28 10.60 1.17 92.50 54.35 3.48 6.53 9.61 8.34 1.85 2.83 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
21 8 0 43 younger Male High 2 Yes No BTT HMII Alive s/p OHT alive 515760 266115 51.60 96.30 69.66 8.57 72.22 27.78 6.26 4.77 43.09 26.01 28.26 2.63 16.67 2.67 28.25 50.35 0.41 12.78 4.47 84.76 19.47 9.56 4.55 8.29 0.87 2.18 0.75 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
22 8 8 43 younger Male High 2 Yes No BTT HMII Alive s/p OHT alive 539521 223872 41.49 96.72 60.02 12.63 80.13 19.87 1.66 2.48 43.56 19.52 35.24 1.68 20.46 3.67 20.54 55.76 0.18 10.80 2.43 87.82 13.25 6.55 5.96 4.65 0.95 2.51 0.65 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
23 8 21 43 younger Male High 2 Yes No BTT HMII Alive s/p OHT alive 564317 216312 38.33 97.40 55.80 4.83 74.19 25.81 5.35 5.77 37.90 25.72 34.76 1.62 17.99 2.18 31.75 61.61 0.77 13.44 6.97 76.55 6.97 10.34 11.14 4.46 0.94 5.14 2.43 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
24 10 0 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 92221 27988 30.35 85.45 55.78 1.51 85.56 14.44 0.28 0.28 55.28 4.72 39.72 0.28 0.00 1.89 1.89 89.44 0.00 2.78 0.00 15.00 0.00 3.33 0.83 0.28 0.28 3.33 0.00 7.64 8.05 12.160 18.210 2.65 2.810 2.640 2.960 12.830 1.500 2.82 2.930 1.340 0.600 2.050 88.780 4.370 4.15 28.83 39.26 873.00 84.790 386.00 413 361 15.64 102.00 3.080 25.630 12.440 3.660 40.120 112.000 84.03 48.41 2.75 67.22 13502.00
25 10 1 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 273746 46755 17.08 93.25 50.14 7.57 88.27 11.73 0.09 2.12 54.52 3.36 42.00 0.12 8.40 6.87 0.00 92.00 0.12 3.45 0.00 3.94 11.43 1.12 0.64 2.76 0.33 2.15 0.00 1.74 3.20 8.390 13.870 2.65 2.810 2.640 2.960 5.480 1.500 2.63 1.040 0.420 0.020 0.960 56.230 6.110 2.78 12.80 22.38 257.00 109.000 212.00 207 352 2.90 59.32 3.080 10.480 4.350 2.300 35.160 31.280 16.68 40.19 1.22 37.18 3646.00
26 10 3 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 156053 71752 45.98 78.38 35.43 27.12 78.57 21.43 0.24 0.60 58.56 5.80 18.33 17.31 12.67 72.96 0.53 94.06 0.49 5.11 0.28 22.79 27.47 0.81 5.53 0.01 0.29 0.19 0.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
27 10 5 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 28.26 29.24 25.930 19.130 12.01 9.860 4.910 2.960 12.270 2.300 8.31 8.480 4.420 6.460 5.600 31.690 12.300 6.41 65.76 22.02 605.00 111.000 650.00 279 320 27.98 245.00 8.330 34.600 26.450 10.410 112.000 184.000 48.97 197.00 24.56 128.00 8387.00
28 10 8 43 younger Male Low 3 No No BTT HMII Alive s/p OHT alive 156053 71752 45.98 78.38 35.43 27.12 78.57 21.43 0.24 0.60 58.56 5.80 18.33 17.31 12.67 72.96 0.53 94.06 0.49 5.11 0.28 22.79 27.47 0.81 5.53 0.01 0.29 0.19 0.00 70.58 65.90 45.390 26.340 33.60 24.140 10.790 5.940 18.220 4.580 16.86 22.040 10.800 17.200 14.210 33.990 19.940 11.61 146.00 23.83 661.00 105.000 765.00 325 270 45.41 414.00 17.810 51.840 49.970 22.030 205.000 266.000 76.21 266.00 48.57 276.00 9997.00
29 13 0 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 411616 119210 28.96 90.82 73.63 5.88 74.30 25.70 2.80 6.46 57.94 19.20 18.72 4.13 19.10 15.42 15.30 53.51 13.78 25.19 0.21 47.23 53.77 5.75 5.92 22.63 3.46 2.55 0.74 74.33 630.00 1198.000 29.033 285.00 6.710 41.890 282.000 58.746 108.000 242.00 1363.000 17.372 74.166 4.089 381.000 11.334 76.55 183.00 145.00 703.00 339.000 938.00 642 424 191.00 439.00 51.380 409.000 169.000 31.768 254.000 3217.000 299.00 1084.00 546.00 877.00 6887.00
30 13 1 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 430509 79492 18.46 89.31 52.10 20.45 87.13 12.87 1.23 4.76 58.11 9.15 30.55 2.20 29.86 18.05 10.37 63.63 10.23 17.43 0.06 74.05 72.50 2.36 2.98 10.68 1.72 1.03 2.89 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
31 13 3 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 395713 119670 30.24 97.90 23.79 6.27 82.17 17.83 0.79 1.99 49.95 13.01 33.91 3.13 28.36 16.83 5.00 56.33 7.13 26.73 0.02 79.50 82.88 1.96 5.55 16.30 3.61 1.88 0.42 4.28 159.00 409.000 41.124 12.27 2.320 4.307 20.356 15.581 16.793 121.00 61.854 2.899 4.794 1.980 49.157 20.020 28.46 32.56 102.00 524.00 415.000 305.00 282 465 41.67 203.00 12.431 121.000 35.967 2.120 73.622 945.000 39.60 451.00 55.87 159.00 676.00
32 13 8 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 329840 73468 22.27 98.63 70.61 3.73 51.50 48.50 8.32 3.77 33.46 41.29 20.92 4.33 25.19 5.89 24.07 46.40 3.73 34.64 0.07 64.14 28.43 10.35 9.61 38.78 4.62 5.55 2.91 100.00 430.00 1063.000 57.719 245.00 135.000 21.182 84.771 41.826 160.000 229.00 580.000 22.030 72.412 10.670 229.000 25.806 54.08 223.00 143.00 966.00 310.000 777.00 1415 796 217.00 325.00 45.047 411.000 137.000 44.518 266.000 2406.000 193.00 908.00 321.00 732.00 7464.00
33 13 14 61 older Male High 2 No No BTT HMII Alive s/p OHT alive 350294 88823 25.36 97.63 74.37 4.37 73.35 26.65 4.91 4.83 56.54 21.32 19.84 2.30 34.68 11.25 18.37 61.98 5.78 20.55 0.04 63.91 47.54 6.81 6.17 21.77 2.90 2.74 3.00 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
34 16 0 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 52016 30920 59.44 95.77 55.57 24.62 58.73 41.27 2.14 0.97 53.01 5.05 5.45 36.50 9.39 81.29 2.88 81.48 5.65 9.02 0.48 97.75 71.83 0.67 22.89 0.34 1.78 0.49 NA 11.66 5.99 5.180 18.210 2.65 2.810 2.640 2.960 2.600 1.500 3.19 3.790 0.750 1.140 1.270 12.840 1.920 2.78 22.29 13.36 205.00 120.000 433.00 852 235 13.38 108.00 17.980 22.860 7.890 3.660 26.920 114.000 27.36 63.49 6.10 47.28 2960.00
35 16 1 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 75516 48801 64.62 92.13 52.90 18.64 70.39 29.61 2.92 2.21 62.18 5.13 7.98 24.70 13.79 69.54 3.19 84.14 3.64 6.36 0.41 94.93 43.78 0.57 15.61 0.23 0.91 0.36 NA 6.69 4.54 3.850 13.310 2.65 2.810 2.640 2.960 15.360 1.500 2.89 2.390 0.640 1.550 1.060 51.270 3.890 2.78 23.91 29.12 139.00 53.210 184.00 503 192 13.38 162.00 30.710 25.630 7.270 3.380 58.260 135.000 10.50 52.34 4.60 111.00 1306.00
36 16 3 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 250612 130454 52.05 95.75 60.05 3.29 46.04 53.96 1.34 3.89 33.08 32.47 8.29 26.15 3.56 34.61 0.09 76.71 0.39 13.20 0.28 39.79 14.38 2.26 1.56 10.45 2.89 3.04 0.08 22.58 5.49 6.580 15.690 2.77 2.810 2.640 2.960 19.820 1.500 3.96 4.710 1.750 5.060 1.270 242.000 7.410 6.23 43.70 45.47 205.00 139.000 201.00 614 498 27.61 259.00 37.910 41.540 13.790 4.520 90.330 187.000 20.67 91.18 20.95 94.90 1637.00
37 16 5 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 36.87 8.59 9.500 14.150 5.49 4.340 2.640 2.960 3.860 1.500 4.92 8.120 2.050 5.620 1.480 49.360 5.860 3.77 51.10 19.90 214.00 142.000 261.00 1163 556 27.24 145.00 37.770 41.540 20.750 5.700 54.980 210.000 28.80 99.93 38.44 100.00 3060.00
38 16 8 39 younger Female Low 3 Yes No BTT HMII Died post OHT dead 212478 120477 56.70 85.12 48.36 12.51 79.57 20.43 1.35 2.14 58.73 12.87 18.44 9.96 5.63 34.32 0.11 86.74 0.41 6.88 0.15 35.30 53.09 1.23 0.75 2.36 1.04 1.47 NA 39.75 7.52 10.260 14.990 6.70 4.900 2.640 3.060 5.960 1.500 5.57 11.870 2.670 6.610 1.480 49.000 5.860 3.86 55.20 17.87 278.00 105.000 428.00 1180 309 30.12 162.00 41.370 49.350 22.170 5.990 67.210 220.000 34.39 108.00 31.81 119.00 3873.00
39 17 0 70 older Female High 2 No NA DT HMII Alive alive 86135 70306 81.62 93.71 74.50 1.59 61.74 38.26 1.91 2.67 33.97 14.50 26.81 24.71 20.67 62.47 9.03 73.00 2.00 24.71 1.27 20.23 71.43 0.48 12.79 0.38 3.24 0.48 4.60 2.81 7.26 7.290 173.000 2.65 2.810 2.640 2.960 12.550 1.500 4.28 8.850 0.640 0.770 1.160 2.930 3.890 3.28 27.19 22.83 145.00 70.170 765.00 1478 482 9.57 94.83 94.860 103.000 8.520 3.660 18.060 120.000 19.71 40.19 28.19 51.59 1276.00
40 17 1 70 older Female High 2 No NA DT HMII Alive alive 81004 41334 51.03 94.06 53.64 2.99 61.70 38.30 3.10 2.75 39.07 13.34 22.12 25.47 12.58 63.11 15.99 78.92 2.93 17.38 3.35 23.24 71.88 0.52 11.70 0.09 1.29 0.52 9.83 7.64 4.08 4.170 41.980 2.65 2.810 2.640 2.960 133.000 1.500 2.63 7.760 0.420 1.770 0.910 98.210 8.740 4.54 15.89 77.14 211.00 64.900 374.00 1714 959 5.00 64.16 21.350 54.290 8.520 2.560 284.000 52.100 11.10 21.92 7.15 51.59 1301.00
41 17 3 70 older Female High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0.22 3.20 3.220 21.580 2.65 2.810 2.640 2.960 15.210 1.500 2.63 2.520 0.420 0.020 0.680 58.680 7.800 3.77 8.37 31.94 83.16 74.380 388.00 644 432 2.90 49.55 8.660 30.220 4.630 1.790 14.940 45.600 11.69 14.26 1.53 29.96 690.00
42 17 5 70 older Female High 2 No NA DT HMII Alive alive 90024 39434 43.80 95.32 68.83 5.62 71.59 28.41 0.85 1.66 35.23 13.02 35.80 15.96 22.94 52.37 5.85 76.42 1.14 21.83 0.62 14.54 48.57 0.38 4.69 0.00 0.80 0.38 0.62 0.58 3.63 3.220 25.010 2.65 2.810 2.640 2.960 23.480 1.500 2.63 2.390 0.420 0.100 0.860 31.820 7.670 11.61 15.89 41.91 454.00 91.330 506.00 670 421 3.21 54.44 14.370 37.430 6.660 2.300 33.500 58.260 15.08 19.42 6.10 41.52 1837.00
43 17 8 70 older Female High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 37.61 21.91 44.071 23.633 39.20 9.532 6.428 5.102 32.860 7.062 12.93 20.414 2.167 3.138 5.978 41.851 8.190 4.95 63.21 39.70 328.00 175.000 323.00 808 540 34.88 91.67 13.604 48.125 30.231 5.864 59.949 466.000 81.74 150.00 4.51 195.00 9741.00
44 17 14 70 older Female High 2 No NA DT HMII Alive alive 106791 80532 75.41 88.82 79.92 1.57 62.32 37.68 1.70 4.02 28.04 22.86 34.11 15.00 23.92 39.41 15.03 61.61 3.66 34.55 1.97 20.36 73.33 0.80 8.57 0.27 1.79 0.98 0.71 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
45 18 0 63 older Male High 1 No NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 60.38 32.81 186.000 38.833 32.98 21.709 3.051 5.621 37.885 11.091 29.33 189.000 6.338 16.504 4.089 278.000 17.668 7.17 59.99 54.99 695.00 232.000 211.00 828 1145 50.86 107.00 10.251 111.000 31.722 7.153 120.000 533.000 28.75 322.00 2.34 224.00 3524.00
46 18 1 63 older Male High 1 No NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 63.95 40.14 249.000 28.453 32.98 21.709 3.357 4.711 28.562 11.091 38.39 220.000 6.089 13.032 4.089 124.000 15.114 5.01 59.99 41.74 638.00 289.000 303.00 509 713 59.63 121.00 9.867 123.000 56.352 7.153 82.196 685.000 46.72 384.00 14.39 229.00 5489.00
47 18 3 63 older Male High 1 No NA BTT HMII Died dead 366713 77282 21.07 98.61 66.55 12.44 93.65 6.35 0.53 0.60 62.22 4.68 32.38 0.72 19.94 9.89 28.23 83.18 0.51 14.38 0.30 94.72 54.39 2.07 11.33 2.86 3.40 1.48 3.58 65.71 24.75 198.000 26.329 40.68 18.837 4.631 4.711 20.178 9.696 29.66 185.000 5.841 11.411 4.293 71.642 12.166 3.92 74.20 35.60 1110.00 222.000 300.00 489 627 50.86 126.00 9.479 104.000 51.467 10.275 77.933 727.000 62.42 422.00 11.03 219.00 9557.42
48 18 5 63 older Male High 1 No NA BTT HMII Died dead 262251 55344 21.10 99.55 62.37 14.39 93.00 7.00 1.03 0.69 65.71 4.74 28.37 1.17 25.93 17.28 25.93 79.87 1.20 17.05 0.48 97.76 58.18 2.27 11.13 4.12 4.91 1.87 6.10 89.92 70.33 309.000 36.344 103.00 29.010 8.732 7.037 28.329 24.712 57.19 384.000 8.403 25.681 5.347 93.592 18.950 6.40 67.14 51.06 434.00 238.000 328.00 896 536 91.53 134.00 14.161 155.000 92.272 10.890 115.000 873.000 64.87 398.00 65.30 368.00 2247.00
49 18 8 63 older Male High 1 No NA BTT HMII Died dead 309193 61091 19.76 98.15 56.62 7.24 83.61 16.39 3.27 1.52 58.03 11.90 28.13 1.93 24.65 11.36 1.52 79.53 1.45 13.33 0.97 92.63 25.76 3.50 9.23 7.57 3.73 3.48 0.00 60.38 17.64 183.000 34.233 42.23 20.268 6.645 4.488 29.029 11.795 27.05 177.000 6.089 10.605 3.887 154.000 12.585 3.77 56.39 56.34 1534.00 165.000 446.00 487 478 53.19 121.00 10.759 85.242 37.382 13.310 79.359 700.000 104.00 490.00 12.72 151.00 9557.42
50 19 0 68 older Male Low 3 No NA DT HMII Died dead 147997 54649 36.93 91.75 79.63 7.62 74.86 25.14 0.60 3.45 62.45 22.87 12.30 2.38 45.98 9.28 13.61 74.33 2.67 21.19 0.84 91.31 90.15 3.58 2.25 21.79 1.36 0.58 0.34 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
51 19 1 68 older Male Low 3 No NA DT HMII Died dead 177687 56400 31.74 97.13 69.76 12.60 81.83 18.17 0.65 1.61 55.77 16.13 26.02 2.09 53.19 10.70 11.57 82.92 1.38 13.98 0.28 89.83 85.59 3.78 1.85 14.65 1.30 0.68 0.18 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
52 19 3 68 older Male Low 3 No NA DT HMII Died dead 233155 67140 28.80 98.44 74.89 5.32 81.96 18.04 1.00 1.88 49.03 16.59 32.90 1.48 49.92 7.20 12.83 83.44 1.39 12.98 0.14 89.70 69.70 4.58 1.62 13.89 1.08 1.08 0.17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
53 19 5 68 older Male Low 3 No NA DT HMII Died dead 99257 29455 29.68 97.33 62.48 4.73 80.60 19.40 1.84 1.25 45.06 18.36 35.25 1.33 44.61 6.32 12.27 82.89 0.88 13.20 0.09 83.55 35.29 7.23 3.54 14.75 2.29 3.02 1.20 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
54 19 8 68 older Male Low 3 No NA DT HMII Died dead 76358 18702 24.49 97.32 29.48 11.10 86.29 13.71 2.43 0.50 37.92 12.13 48.32 1.63 46.45 11.70 11.35 83.96 0.20 10.50 0.06 57.82 50.00 9.95 29.60 10.20 0.64 9.36 0.80 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
55 21 0 46 younger Male High 2 No NA DT HMII Alive alive 226762 25868 11.41 89.41 25.31 12.05 55.06 44.94 11.31 2.69 14.79 42.57 39.95 2.69 19.87 3.88 32.88 68.95 10.48 13.57 0.59 80.62 22.67 31.91 19.38 15.58 10.37 16.55 8.18 2.18 2.45 60.794 34.617 2.31 2.320 1.245 1.590 68.832 1.520 7.45 2.120 1.920 1.450 1.980 130.000 3.544 14.61 5.57 47.27 442.00 52.814 249.00 328 245 3.98 55.48 2.230 19.937 2.737 3.987 21.566 118.000 2.89 23.91 0.39 52.69 1293.00
56 21 3 46 younger Male High 2 No NA DT HMII Alive alive 244049 39386 16.14 94.94 40.46 13.23 40.93 59.07 13.36 3.17 8.33 54.90 32.28 4.49 18.52 4.21 23.83 55.18 13.87 19.39 1.44 70.53 35.67 33.62 33.47 22.86 15.26 25.81 5.11 2.18 2.71 17.955 26.715 9.57 2.320 1.893 1.590 3.759 1.520 3.89 2.120 1.920 1.450 1.980 59.228 1.623 7.17 8.93 60.31 820.00 73.754 165.00 347 262 3.98 63.62 2.230 18.052 2.737 3.358 15.234 90.169 11.56 77.19 0.39 33.64 2726.00
57 21 5 46 younger Male High 2 No NA DT HMII Alive alive 344736 250121 72.55 89.83 7.54 3.81 45.84 54.16 3.96 1.53 13.39 52.70 32.18 1.72 67.14 2.92 2.44 74.68 6.81 13.91 0.21 63.33 58.02 33.05 30.94 19.83 13.96 26.74 0.22 2.18 4.12 15.278 24.202 12.27 2.320 2.457 1.590 4.492 1.520 3.07 2.120 1.920 1.450 2.558 43.554 1.623 2.86 23.32 48.13 1082.00 71.954 125.00 260 251 5.90 77.95 2.230 18.052 7.234 7.153 27.925 153.000 29.97 118.00 0.39 52.69 4714.00
58 21 8 46 younger Male High 2 No NA DT HMII Alive alive 216934 140093 64.58 96.37 4.78 7.22 22.49 77.51 13.49 0.64 7.22 67.61 15.17 10.00 35.88 7.82 17.22 51.28 39.40 6.56 1.33 62.59 58.06 66.67 64.59 53.00 50.26 63.78 4.97 2.18 2.45 24.797 23.042 8.26 2.320 1.625 1.590 3.060 1.520 4.03 2.120 1.920 1.450 1.980 44.329 0.827 2.13 5.57 54.35 370.00 84.966 89.52 348 255 3.98 46.41 2.230 9.990 2.520 2.120 9.035 55.097 2.89 23.91 0.39 27.20 1113.00
59 21 14 46 younger Male High 2 No NA DT HMII Alive alive 215820 66785 30.94 92.54 17.20 6.01 25.88 74.12 9.15 1.45 3.09 71.72 22.44 2.74 65.43 3.42 0.04 69.38 15.93 8.85 0.74 50.58 24.07 52.78 48.88 32.28 28.19 44.04 0.04 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
60 21 21 46 younger Male High 2 No NA DT HMII Alive alive 670692 523014 77.98 96.05 5.07 2.64 23.61 76.39 4.11 0.49 4.79 73.53 18.68 3.00 42.40 2.72 4.49 71.95 20.81 5.51 0.45 48.22 44.62 60.13 60.26 39.07 36.82 56.38 1.49 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
61 22 0 75 older Male High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 59.97 33.76 44.886 35.293 48.50 30.282 52.148 11.702 46.900 18.224 10.78 72.616 5.569 5.458 11.016 23.895 17.965 7.15 143.00 43.59 989.00 135.000 584.00 662 925 40.52 163.00 13.354 43.750 59.591 13.629 94.278 449.000 104.00 196.00 0.92 248.00 10443.75
62 22 1 75 older Male High 2 No NA DT HMII Alive alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 6.42 13.61 24.600 30.292 4.17 3.956 10.044 3.491 27.540 3.964 5.69 3.190 0.940 1.805 2.360 69.905 14.225 3.22 64.43 56.18 263.00 47.791 495.00 624 1321 16.57 80.75 6.352 20.019 15.869 3.706 57.971 119.000 6.20 184.00 0.92 49.17 2297.00
63 22 3 75 older Male High 2 No NA DT HMII Alive alive 121794 28129 23.10 97.05 52.79 16.42 70.08 29.92 0.29 0.54 50.71 21.46 22.96 4.86 34.18 12.51 23.49 39.78 0.31 55.18 0.35 96.01 16.67 1.74 5.27 23.38 2.95 1.00 0.10 2.77 13.90 16.630 13.668 9.16 2.970 3.372 2.078 10.277 6.347 2.66 4.260 1.165 1.090 2.360 36.910 7.340 2.59 60.77 27.10 698.00 100.000 514.00 323 606 25.83 54.27 4.276 23.867 33.318 4.024 31.548 264.000 61.66 124.00 0.92 69.60 10443.75
64 22 5 75 older Male High 2 No NA DT HMII Alive alive 82157 17295 21.05 99.20 72.54 7.41 68.21 31.79 1.10 0.55 48.47 22.90 23.60 5.04 31.90 13.57 18.10 37.84 0.47 56.73 0.35 92.37 14.29 2.05 7.79 26.20 4.72 1.42 1.02 17.15 11.58 21.356 25.652 9.57 5.509 5.963 3.002 17.410 6.315 3.89 23.573 2.504 1.494 5.132 87.134 12.166 2.86 56.39 33.82 845.00 150.000 484.00 556 814 19.17 130.00 6.685 27.906 20.483 10.890 40.533 185.000 70.35 99.39 2.34 118.00 7899.00
65 22 8 75 older Male High 2 No NA DT HMII Alive alive 154810 27067 17.48 98.04 72.18 5.03 74.91 25.09 0.90 1.65 58.05 14.76 20.82 6.37 23.43 25.71 22.57 44.94 0.60 49.36 0.71 86.59 22.73 3.30 7.64 20.15 4.87 1.72 0.00 10.19 18.30 30.677 25.655 16.47 9.100 6.693 4.896 29.159 3.022 8.07 3.688 1.295 1.557 8.079 68.056 13.360 5.90 58.29 31.79 671.00 47.791 696.00 506 704 18.84 102.00 12.748 17.993 11.877 5.864 44.162 198.000 49.71 115.00 1.39 114.00 10443.75
66 22 21 75 older Male High 2 No NA DT HMII Alive alive 197936 15446 7.80 95.86 50.35 5.44 79.13 20.87 3.98 3.11 52.30 15.90 29.81 1.99 20.23 5.78 24.86 40.62 1.12 50.06 1.89 62.36 8.00 7.20 9.94 13.17 3.60 5.09 1.55 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
67 23 0 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 114586 35996 31.41 100.00 59.03 11.31 75.87 24.13 1.52 7.69 69.83 11.67 10.20 8.30 27.80 29.94 2.04 84.64 0.64 10.54 0.61 91.57 61.34 4.99 7.15 11.57 5.92 2.53 1.92 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
68 23 1 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 13.27 4.09 22.842 12.996 2.80 2.970 1.098 0.346 6.097 2.290 3.48 3.190 0.940 0.850 2.360 103.000 4.849 4.64 2.91 28.25 276.00 25.145 449.00 274 442 7.15 31.49 3.050 10.241 2.290 1.036 12.701 13.318 2.92 110.00 0.92 39.59 1621.00
69 23 3 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 349054 129419 37.08 95.25 62.35 8.81 79.64 20.36 2.72 4.45 57.70 13.93 25.89 2.48 57.14 13.49 8.69 74.29 3.76 12.35 1.41 85.87 62.53 3.41 5.43 7.67 1.40 1.92 1.06 72.99 13.61 25.476 15.689 9.16 4.312 3.544 2.168 12.912 2.432 6.98 3.190 1.430 0.850 5.769 30.560 4.849 4.20 59.53 23.70 268.00 56.470 507.00 350 496 15.38 108.00 3.050 23.400 13.860 7.400 61.926 158.000 3.85 123.00 0.92 108.00 2374.00
70 23 5 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 447309 176789 39.52 93.00 53.57 9.32 80.92 19.08 2.16 5.73 56.72 12.51 28.43 2.34 47.68 12.19 11.00 73.80 4.44 12.53 2.57 83.88 49.09 5.24 6.82 8.32 2.18 2.28 1.72 75.52 39.53 49.720 16.363 80.35 13.585 14.246 8.044 28.002 22.225 15.11 23.132 3.869 4.279 13.928 30.947 13.071 8.12 149.00 16.40 384.00 120.000 681.00 419 400 41.84 179.00 5.172 46.471 46.582 14.665 126.000 364.000 36.57 89.46 22.99 224.00 4716.00
71 23 8 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 558290 301468 54.00 95.62 47.73 7.04 79.13 20.87 3.43 7.49 53.19 13.48 30.32 3.01 36.36 14.54 12.10 70.55 5.15 11.89 3.51 79.79 52.89 6.98 8.50 7.82 1.92 3.09 1.96 55.93 9.15 32.389 17.862 2.80 2.970 3.716 1.989 10.711 2.290 7.10 NA NA 12.466 2.360 17.125 3.019 4.20 57.05 18.04 905.00 265.000 855.00 393 618 22.99 71.07 3.050 22.929 32.290 4.131 50.068 170.000 NA 97.14 17.70 80.29 10443.75
72 23 14 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 479402 362845 75.69 97.95 44.30 6.30 76.53 23.47 3.90 11.10 34.99 12.04 50.31 2.66 21.93 14.88 10.05 70.21 4.66 7.31 7.73 49.96 29.38 13.44 17.37 5.64 3.01 6.95 2.28 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
73 23 21 30 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 334942 191418 57.15 93.28 70.35 4.04 70.89 29.11 6.20 17.70 56.45 19.57 20.44 3.53 38.91 14.64 10.35 55.11 6.61 14.34 8.70 80.96 58.92 7.58 7.96 10.11 2.08 2.08 2.48 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
74 24 0 67 older Male High 2 Yes NA BTT HMII Died dead 129980 41600 32.00 91.40 39.03 2.85 49.63 50.37 7.02 11.55 28.37 29.11 20.79 21.72 19.43 28.34 9.80 69.59 4.90 20.33 6.13 31.15 33.60 20.15 22.27 0.28 4.25 16.54 2.09 33.99 28.59 16.040 25.430 12.01 7.920 6.660 3.530 19.960 3.020 7.39 10.910 3.870 5.620 7.350 29.050 11.190 6.77 80.92 26.34 485.00 156.000 684.00 574 531 28.71 233.00 9.210 37.430 28.250 15.220 112.000 158.000 41.01 59.87 50.92 139.00 4188.00
75 24 1 67 older Male High 2 Yes NA BTT HMII Died dead 59478 20182 33.93 89.07 44.50 10.08 60.93 39.07 2.10 2.70 42.88 13.69 17.66 25.77 13.06 46.12 3.13 81.35 1.16 16.11 0.83 37.58 6.12 5.52 11.64 0.06 1.49 2.76 4.07 4.02 3.20 2.320 19.970 2.65 2.810 2.640 2.960 405.000 1.500 2.63 2.130 0.460 0.450 0.860 99.660 13.000 2.78 12.80 206.00 266.00 84.610 329.00 197 841 3.21 39.72 8.990 27.180 6.360 2.830 565.000 23.210 11.10 10.23 1.22 38.63 1588.00
76 24 3 67 older Male High 2 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 6.69 9.68 5.520 72.480 2.65 2.810 2.640 2.960 12.830 1.500 3.81 3.490 3.700 1.140 1.870 30.080 8.600 3.57 26.36 120.00 339.00 166.000 633.00 484 808 12.17 90.21 79.330 79.230 12.440 4.520 64.780 75.100 48.83 38.04 18.56 55.88 3892.00
77 24 5 67 older Male High 2 Yes NA BTT HMII Died dead 26646 9754 36.61 98.69 58.32 7.57 64.88 35.12 2.06 0.55 46.50 12.62 17.70 23.18 15.07 53.31 12.50 83.40 1.10 15.64 1.76 28.12 0.00 7.54 16.46 0.55 1.92 6.04 13.00 0.58 3.20 3.220 34.140 2.65 2.810 2.640 2.960 39.370 1.500 2.63 2.930 0.420 0.020 0.540 66.160 8.470 3.38 5.65 51.46 66.05 106.000 497.00 1069 1138 2.90 29.91 6.510 29.470 5.480 1.550 51.690 2.870 3.21 8.86 43.82 41.52 535.00
78 24 8 67 older Male High 2 Yes NA BTT HMII Died dead 73641 21944 29.80 96.79 48.91 4.47 62.74 37.26 2.84 3.16 37.47 17.79 24.95 19.79 19.46 38.11 9.46 79.79 1.89 13.58 1.24 27.05 20.00 14.11 13.37 0.21 2.74 10.53 3.31 2.44 4.08 4.340 49.370 2.65 2.810 2.640 2.960 25.260 1.500 2.63 6.360 0.420 0.100 0.770 74.530 10.920 5.20 8.37 91.97 73.49 135.000 493.00 1207 1273 2.90 49.55 17.980 28.710 7.270 2.560 35.160 14.190 3.93 8.86 52.09 35.74 551.00
79 25 0 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 44004 20612 46.84 43.65 15.61 21.54 49.64 50.36 10.37 0.57 15.94 45.15 33.18 5.73 24.69 5.68 20.25 44.58 1.55 36.17 0.88 67.23 27.27 32.35 15.48 20.02 4.49 10.94 2.76 71.72 47.35 26.349 37.381 158.00 33.334 10.660 8.044 35.402 46.417 6.87 NA 10.690 8.792 2.398 99.342 9.045 6.48 97.08 46.45 297.00 181.000 191.00 567 222 49.74 80.75 6.059 37.993 25.593 20.817 174.000 399.000 44.78 92.77 41.52 NA 4980.00
80 25 1 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 99731 23424 23.49 91.81 41.33 10.89 35.24 64.76 11.83 1.62 18.71 59.21 16.19 5.89 28.16 5.89 18.53 39.98 4.66 42.16 2.33 84.07 2.63 22.55 15.21 40.28 6.07 9.70 3.96 29.43 17.11 20.632 25.281 48.50 22.252 5.900 4.692 222.000 11.168 3.91 72.616 4.422 2.060 2.360 191.000 11.918 7.37 68.02 138.00 324.00 136.000 149.00 347 223 30.17 80.75 4.036 30.061 12.370 7.619 280.000 331.000 21.43 70.73 2.56 274.00 3581.00
81 25 3 68 older Male High 2 No No BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 74.26 46.69 17.521 32.607 125.00 34.355 9.340 7.512 18.328 13.879 4.56 190.000 13.031 14.904 2.360 35.125 11.629 8.85 37.42 35.14 218.00 222.000 173.00 1649 290 71.92 85.48 3.557 52.272 44.557 15.485 186.000 514.000 30.19 168.00 48.69 743.00 1283.00
82 25 5 68 older Male High 2 No No BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 63.06 27.96 35.932 31.732 44.55 16.008 3.051 5.621 18.791 10.391 5.03 152.000 11.673 14.663 1.980 35.584 10.919 6.56 45.46 42.92 420.00 180.000 216.00 2058 291 95.02 121.00 5.314 63.342 43.034 23.444 164.000 411.000 28.75 257.00 28.67 330.00 1677.00
83 25 8 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 129637 50001 38.57 90.65 44.41 6.05 30.66 69.34 17.12 4.05 10.62 66.50 19.60 3.28 26.41 3.46 13.37 39.74 7.15 39.49 11.91 71.97 2.70 32.48 33.14 43.91 7.81 22.70 3.82 40.52 23.75 22.842 21.835 74.36 13.123 5.021 5.931 27.077 9.272 4.67 134.000 12.603 9.710 2.360 53.765 8.759 6.48 60.77 35.38 426.00 223.000 224.00 1195 211 57.75 93.20 3.857 32.945 29.201 15.485 185.000 523.000 35.17 121.00 27.51 602.00 4088.00
84 25 14 68 older Male High 2 No No BTT HMII Alive s/p OHT alive 134368 46363 34.50 88.32 34.25 4.63 32.47 67.53 12.86 8.38 8.91 64.89 22.88 3.32 24.78 3.89 13.58 36.16 6.01 40.33 13.77 72.11 1.89 29.89 26.36 42.80 6.17 15.97 3.29 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
85 27 0 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 59881 8971 14.98 97.40 50.06 9.57 75.84 24.16 3.59 8.13 64.83 13.40 15.67 6.10 19.31 18.81 16.83 76.67 6.10 6.82 2.00 88.16 47.06 10.53 11.24 10.41 9.93 6.94 11.93 16.38 67.35 180.000 62.930 127.00 2.970 36.468 4.794 25.690 5.645 46.84 139.000 1.295 17.624 2.360 148.000 9.904 9.46 143.00 64.82 261.00 507.000 809.00 891 476 76.81 136.00 18.740 113.000 98.896 20.817 358.000 925.000 91.47 168.00 123.00 653.00 761.00
86 27 1 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 60193 23874 39.66 96.72 58.83 16.24 80.37 19.63 1.87 3.31 68.58 10.62 14.72 6.08 21.33 22.83 3.80 80.61 3.63 8.03 0.33 94.16 57.26 2.96 5.01 10.19 4.72 1.41 1.24 3.57 32.49 145.000 59.668 35.43 2.970 12.151 4.287 29.390 5.645 54.69 26.852 0.940 6.660 2.360 142.000 12.494 6.13 91.76 118.00 294.00 354.000 659.00 811 759 35.98 83.92 12.544 90.832 44.557 39.375 223.000 830.000 67.87 147.00 58.83 260.00 528.00
87 27 3 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 8.66 39.21 135.000 67.453 61.20 3.273 20.290 5.826 23.842 5.819 45.26 64.203 1.569 9.863 3.090 66.315 10.766 5.31 155.00 68.00 355.00 420.000 945.00 1063 603 56.22 136.00 16.032 94.647 49.105 19.468 230.000 804.000 72.71 206.00 83.10 435.00 814.00
88 27 5 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 135335 27834 20.57 97.35 44.75 5.61 63.22 36.78 8.49 17.63 39.01 23.16 30.72 7.11 17.53 10.38 12.16 42.70 4.93 19.28 4.68 67.37 4.10 26.64 17.76 19.21 14.54 14.47 10.60 2.18 38.51 215.000 76.341 16.50 2.320 10.862 2.224 8.932 1.520 53.96 36.506 1.920 7.437 1.980 105.000 8.874 7.63 77.70 68.16 389.00 204.000 723.00 1321 487 50.26 160.00 12.674 125.000 27.485 45.764 177.000 710.000 55.82 360.00 90.56 239.00 351.00
89 27 8 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 247113 62042 25.11 96.41 43.93 14.82 78.42 21.58 2.52 5.24 65.35 12.22 17.53 4.90 20.39 17.20 2.93 77.86 2.11 8.78 2.94 84.64 9.70 10.64 13.93 9.90 11.59 8.48 3.51 22.57 29.97 91.301 58.481 24.03 7.004 8.899 5.515 27.077 7.972 25.93 37.523 2.650 3.138 5.142 70.641 12.783 7.59 94.97 56.14 551.00 288.000 717.00 1003 373 39.51 130.00 9.800 75.273 27.655 7.838 145.000 524.000 52.18 137.00 41.89 251.00 338.00
90 27 14 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 142053 56983 40.11 93.01 62.72 12.87 85.10 14.90 0.69 1.64 68.02 8.40 21.03 2.55 17.03 13.68 1.08 83.33 0.37 7.38 0.26 79.00 16.07 3.01 5.85 5.16 5.24 1.77 0.70 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
91 27 21 64 older Male High 2 No Yes BTT HMII Alive s/p OHT alive 60384 17029 28.20 93.65 40.96 21.36 83.80 16.20 1.47 2.32 63.52 9.69 23.80 2.99 23.55 15.58 0.91 78.16 1.09 8.13 1.00 83.65 17.72 4.70 8.42 5.99 7.22 3.76 1.56 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
92 28 0 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 47645 37377 78.45 94.61 2.47 9.41 78.56 17.02 1.71 4.63 34.94 16.09 44.77 4.21 4.35 28.99 59.42 67.26 3.10 26.22 7.50 72.82 53.90 13.50 28.20 3.04 7.70 7.67 0.96 9.61 35.14 22.350 20.460 3.79 22.660 3.480 3.530 9.020 3.210 9.75 7.060 1.210 1.550 0.770 20.320 2.980 2.78 20.67 13.18 290.00 37.840 754.00 653 382 20.62 170.00 9.210 42.210 9.160 3.660 54.980 131.000 33.28 78.76 74.92 178.00 1807.00
93 28 1 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 87452 43025 49.20 91.54 1.98 26.71 92.06 6.76 0.77 4.67 47.21 4.70 45.17 2.92 6.06 18.18 60.61 81.01 3.95 14.10 12.04 85.97 79.84 5.82 14.15 1.11 4.77 2.84 0.00 12.71 13.67 11.390 16.950 2.77 3.020 2.690 2.960 29.730 1.790 5.89 3.490 1.610 2.220 2.640 103.000 6.370 5.67 25.55 37.67 369.00 23.230 830.00 341 373 18.73 111.00 6.740 23.260 13.120 5.700 76.070 128.000 32.15 77.14 17.37 97.61 3031.00
94 28 3 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 124056 80873 65.19 95.41 0.37 14.36 90.47 5.98 0.39 0.60 41.84 4.26 51.08 2.81 18.82 35.29 43.53 86.97 0.10 11.32 1.10 75.49 12.12 3.80 14.94 0.24 0.88 3.25 0.00 37.44 26.01 28.120 25.360 9.28 13.970 5.470 5.150 38.770 3.780 11.03 16.650 3.350 5.760 5.140 32.780 7.800 5.95 60.09 24.86 338.00 39.760 682.00 2208 349 27.24 174.00 11.700 30.960 38.010 10.120 76.070 213.000 35.85 86.63 43.82 185.00 2088.00
95 28 5 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 27.69 16.65 26.730 24.380 9.95 7.920 2.820 4.000 58.110 1.790 8.23 7.760 1.750 3.200 3.390 44.050 4.860 3.77 51.92 25.90 506.00 38.490 451.00 1325 306 19.69 141.00 6.280 18.330 27.170 7.470 68.020 184.000 35.13 89.68 25.77 160.00 2691.00
96 28 8 25 younger Female Low 3 No Yes BTT HMII Alive s/p OHT alive 87840 57990 66.02 95.60 0.11 15.80 89.00 9.49 1.56 5.02 45.89 7.18 43.54 3.39 6.10 19.51 56.71 84.23 3.14 9.95 10.70 72.13 52.73 7.06 17.53 0.74 3.13 5.13 0.13 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
97 30 0 49 younger Male Low 3 No NA DT HMII Alive alive 196315 122053 62.17 95.37 60.66 22.66 91.10 8.90 0.45 4.95 85.57 6.30 5.42 2.71 13.13 28.32 20.05 86.32 2.45 5.42 0.96 98.68 94.19 2.50 2.96 4.63 1.24 0.33 0.89 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
98 30 1 49 younger Male Low 3 No NA DT HMII Alive alive 91909 37622 40.93 96.10 43.22 38.85 95.48 4.52 0.14 2.19 85.07 3.09 10.33 1.51 21.03 27.23 9.08 88.84 1.13 3.91 0.23 98.77 88.27 1.16 1.16 2.34 0.38 0.21 0.45 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
99 30 5 49 younger Male Low 3 No NA DT HMII Alive alive 153762 86281 56.11 95.83 65.54 24.24 91.12 8.88 0.65 2.84 81.43 6.30 9.62 2.65 21.14 28.73 15.89 83.00 1.97 6.74 0.36 98.22 93.16 3.20 2.29 5.47 1.11 0.77 1.56 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
100 30 8 49 younger Male Low 3 No NA DT HMII Alive alive 82406 47485 57.62 92.71 65.75 17.46 91.79 8.21 1.08 1.99 76.35 5.70 15.41 2.54 20.56 26.95 11.99 78.67 1.35 7.97 0.28 97.55 85.62 2.65 2.56 5.19 1.18 0.86 1.79 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
101 32 0 72 older Male Low 4 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 2.98 33.241 14.342 3.42 2.970 0.420 0.853 3.452 2.290 13.82 3.190 0.940 0.850 2.360 50.340 2.770 5.07 17.40 64.31 653.00 26.894 241.00 316 496 12.21 15.96 3.050 12.001 8.034 2.282 17.749 119.000 3.85 236.00 0.92 44.32 1793.00
102 32 1 72 older Male Low 4 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 24.67 100.000 10.761 20.24 2.970 8.281 0.853 136.000 3.964 19.25 3.688 0.940 1.090 2.360 130.000 11.629 6.13 45.48 139.00 541.00 88.075 437.00 264 768 20.97 77.56 5.231 40.568 66.481 1.717 408.000 245.000 55.44 141.00 16.95 74.91 10443.75
103 32 3 72 older Male Low 4 Yes NA BTT HMII Died dead 424375 21971 5.18 71.78 56.98 18.53 79.84 20.16 3.08 0.51 57.91 14.07 24.06 3.97 31.74 15.95 13.65 57.94 0.65 28.51 0.17 93.57 53.33 2.87 9.69 15.61 3.87 2.16 3.99 2.18 6.65 125.000 14.770 4.61 2.320 14.644 1.590 22.499 1.520 18.98 2.120 1.920 1.450 1.980 72.656 11.749 1.99 23.32 48.13 195.00 286.000 323.00 579 874 10.05 63.62 2.448 45.479 16.356 0.944 52.863 60.307 4.34 61.08 0.39 52.69 813.00
104 32 5 72 older Male Low 4 Yes NA BTT HMII Died dead NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.77 10.24 45.698 19.436 2.80 2.970 13.723 0.458 31.010 2.290 9.91 3.190 0.940 0.850 2.360 109.000 9.904 3.42 31.87 57.12 177.00 35.314 337.00 361 834 30.99 61.08 3.916 39.476 11.877 2.878 32.504 142.000 2.92 154.00 0.92 69.60 1160.00
105 32 8 72 older Male Low 4 Yes NA BTT HMII Died dead 177249 103442 58.36 76.40 15.43 5.64 65.13 34.87 5.21 0.20 35.56 27.91 31.90 4.62 26.60 8.28 13.24 67.89 0.29 20.13 0.00 78.60 22.22 5.50 16.09 25.56 6.80 4.73 0.89 2.18 2.98 74.914 45.496 2.31 2.320 4.146 1.590 63.920 1.520 21.62 2.120 20.861 1.450 1.980 773.000 19.378 33.60 45.46 368.00 630.00 220.000 265.00 523 1437 110.00 102.00 4.899 63.342 35.967 3.045 101.000 80.459 15.43 193.00 0.39 234.00 2752.00

Linear mixed-effect model

We identified biomarkers associated with the categorical variables using a linear mixed effect model. Our model had a random intercept for each subject, and 3 fixed effects for the category, timepoint, and their interaction. This model is equivalent to a two-way repeated measures anova, but also has the ability to fit in the presence of missing data.

Mathematically, this model has the form

\[y_{ij}=b_0 +\sum_{k=1}^p b_k x_{ijk} + v_{i0}+\epsilon_{ij}\]

for \(i\in\{1,...,20\}\) subjects, \(j\in\{1,...,7\}\) measurements for each subject, and \(k\in1,...,p\) predictors or contrasts, where

  • \(y_{ij}\in\mathbb{R}\) is the response for the \(j\)-th measurement of the \(i\)-th subject
  • \(b_0\in\mathbb{R}\) is the fixed intercept for the model
  • \(b_k\in\mathbb{R}\) is the fixed slope for the \(k\)-th predictor or contrast
  • \(x_{ijk}\in\mathbb{R}\) is the \(j\)-th measurement of the \(k\)-th predictor for the \(i\)-th subject
  • \(v_{i0}\sim N(0,\sigma_0^2)\) is the random intercept for the \(i\)-th subject
  • \(e_{ij}\sim N(0,\sigma_e^2)\) is a Gaussian error term

The fundamental assumptions of this model are:

  • the relationship between X and Y is linear
  • \(x_{ijk}\) and \(y_{ij}\) are observed random variables
  • \(v_{i0} \sim N(0,\sigma_v^2)\) is an unobserved random variable, being the random intercept for the \(i\)-th subject
  • \(\epsilon_{ij}\sim N(0,\sigma_e^2)\) is an unobserved random variable, being the Gaussian error term
  • \(v_{i0}\) and \(e_{ij}\) are independent of one another
  • \(b_0\) and \(b_1\) are unknown constants, being the fixed intercept and fixed slope for the regression model
  • \((y_{ij}|x_{ij})\sim N(b_0+b_1 x_{ij}, \sigma_Y^2)\) where \(\sigma_Y^2=\sigma_v^2+\sigma_e^2\)

In implementing the contrasts and statistical tests in R, we followed the advice found here and here, by using zero-sum contrast contr.sum, and obtaining type 3 sums of squares using the car package. Type 3 sums of squares look like \(S(A|B,AB)\), and test a main effect after the other main effect and interaction. The significance estimates are therefore valid in the presence of significant interactions.

We would have also heeded the guidelines on including random slopes for the within factor when testing for interactions, but the number of random effects would have exceeded teh number of observations.

# make sure column names are valid R variables before fitting
df.HMII.valid <- df.HMII
colnames(df.HMII.valid) <- make.names(colnames(df.HMII), unique = T)

# function to fit a two-way repeated measures anova
anova.fit <- function(this_group, this_variable, this_df){
    this_df$Time <- as.factor(this_df$Time)
    this_formula <- as.formula(paste0(this_variable, " ~ ", this_group, " * Time + (1|PatientID)"))
    # set contrasts
    contrasts(this_df[[this_group]]) <- contr.sum 
    contrasts(this_df$Time) <- contr.sum
    # fit model
    this_model <- lmer(this_formula, data = this_df)
    return(list(model = this_model, group = this_group, variable = this_variable))
}

# function to get stats on the model fit
get.anovatable <- function(anova.fit.obj){
    # unpack
    this_model <- anova.fit.obj$model
    this_variable <- anova.fit.obj$variable
    this_group <- anova.fit.obj$group
    # compute p-values
    this_aov <- as.data.frame(Anova(this_model, type="III"))[-1,]
    # annotate with additional variables
    this_aov$parameter <- rownames(this_aov)
    this_aov$variable <- rep(this_variable, nrow(this_aov))
    this_aov$group <- rep(this_group, nrow(this_aov))
    return(this_aov)
}

# create the list of variable ~ group comparisons to run mclapply over
groups.by.vars <- unlist(lapply(make.names(groups, unique = T), function(this_group){
    lapply(make.names(colnames(df.HMII[,bcellcyto]), unique = T), function(this_variable){
        list(group = this_group, variable = this_variable)
    })
}), recursive = F)

# fit all models in parallel
models.HMII <- mclapply(groups.by.vars, function(this_e){
    anova.fit(this_e$group, this_e$variable, df.HMII.valid)
})

# compute statistics in parallel
anovatable.0 <- mclapply(models.HMII, function(this_model_obj){
    get.anovatable(this_model_obj)
}, mc.cores = detectCores()-1)

# include original variable names and add an index for splitting on later
anovatable <- lapply(1:length(anovatable.0), function(ii){
    this_table <- anovatable.0[[ii]]
    name.ix <- match(unique(this_table$variable), colnames(df.HMII.valid))
    this_table$variable.valid <- this_table$variable
    this_table$variable <- rep(colnames(df.HMII)[name.ix], nrow(this_table))
    this_table$testid <- rep(ii, nrow(this_table))
    this_table
})

False Discovery Rate

In all there were 469 models fit, each with 3 terms, for a total of 1407 hypotheses being tested. We computed local false discovery rates and \(q\)-values using the fdrtool package, which produced three figures illustrating the mixture model of the \(p\)-value distribution and the local false discovery rate.

# compute fdr
models <- as.data.frame(do.call(rbind, anovatable))
fdrobj <- fdrtool(models$`Pr(>Chisq)`, statistic = "pvalue", verbose = F)

models$qval <- fdrobj$qval
models$lfdr <- fdrobj$lfdr
anovatable.fdr <- split(models, models$testid)

Results

We reported all results with \(q<0.10\) as statistically significant.

# collect pvalues and qvalues into matrix
models.fdr <- do.call(rbind, anovatable.fdr)
qmat <- dcast(models.fdr, group+parameter~variable, value.var = "qval")
pmat <- dcast(models.fdr, group+parameter~variable, value.var = "Pr(>Chisq)")

# find significant results and mark with text qvalue
qmask <- signif(qmat[,-c(1,2)], 2)
qmask[qmat[,-c(1,2)] > FDRcutoff] <- ""
keep.signif <- apply(t(qmask), 1, function(x) !all(x == ""))
# 
# # create heatmap of results
# pheatmap(-log10(t(qmat[,-c(1,2)])[keep.signif,c(3,1,2,4:nrow(pmat))]),
#          color = colorRampPalette(brewer.pal(n = 9, name = "Greens"))(4),
#          breaks = c(seq(0, -log10(0.1), length.out = 2),
#                     seq(-log10(0.1)+0.001, max(-log10(t(qmat[,-c(1,2)])[keep.signif,])), length.out = 2)),
#          labels_col = pmat$parameter[c(3,1,2,4:nrow(pmat))],
#          display_numbers = t(qmask)[keep.signif,c(3,1,2,4:nrow(pmat))],
#          number_color = "white",
#          fontsize_number = 6,
#          cluster_cols = F,
#          cluster_rows = F,
#          gaps_col = seq(3, nrow(pmat), by = 3),
#          border_color = NA,
#          legend = F,
#          main = paste0("ANOVA results (FDR=", FDRcutoff, ")"))

# gather significant results into a table
resulttable <- do.call(rbind, apply(which(qmask != "", arr.ind = T), 1, function(x){
    data.frame(biomarker = colnames(qmat)[-c(1,2)][x[2]],
               group = qmat$group[x[1]], 
               parameter = qmat$parameter[x[1]], 
               pvalue = pmat[,-c(1,2)][x[1], x[2]], 
               qvalue = qmat[,-c(1,2)][x[1], x[2]])
}))

resulttable.sort <- resulttable[order(resulttable$pvalue), , drop = F]
resulttable.sort[,c("pvalue","qvalue")] <- signif(resulttable.sort[,c("pvalue","qvalue")], 3)
rownames(resulttable.sort) <- 1:nrow(resulttable.sort)
resulttable.sort %>%
    # mutate(
    #     pvalue = cell_spec(pvalue, color = ifelse(resulttable.sort$pvalue > pcutoff, "grey", "green")),
    #     qvalue = cell_spec(qvalue, color = ifelse(resulttable.sort$qvalue > FDRcutoff, "grey", "green"))
    # ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%")
biomarker group parameter pvalue qvalue
1 MCP-1 VAD.Indication VAD.Indication:Time 1.00e-07 0.000112
2 G-CSF RVAD Time 2.00e-07 0.000112
3 MCP-1 VAD.Indication Time 3.00e-07 0.000112
4 CD27+IgD-IgM+ switched memory Sensitized Time 7.00e-07 0.000208
5 num lymph AgeGreater60 AgeGreater60 1.02e-05 0.002530
6 IL-8 RVAD Time 1.00e-04 0.017700
7 G-CSF RVAD RVAD:Time 1.01e-04 0.017900
8 IP-10 VAD.Indication VAD.Indication:Time 1.54e-04 0.023800
9 CD19+CD268+ RVAD Time 5.02e-04 0.053200
10 CD268 of +27-38++transitional AgeGreater60 AgeGreater60:Time 6.17e-04 0.059200
11 GRO Sensitized Time 7.11e-04 0.063300
12 CD19 of live lymph Sex Time 7.78e-04 0.066000
13 CD27+IgD-IgM+ switched memory Sensitized Sensitized:Time 8.15e-04 0.067300
14 CD19+27+IgD-38++IgG ASC RVAD Time 8.60e-04 0.068800
15 TNF-a VAD.Indication VAD.Indication:Time 8.64e-04 0.068900
16 sCD40L Sensitized Time 9.34e-04 0.071200
17 IL-1b LowIntermacs Time 1.20e-03 0.077900
18 CD19+CD268+ AgeGreater60 Time 1.41e-03 0.082000
19 CD19+27+IgD-38++IgG ASC LowIntermacs LowIntermacs 1.51e-03 0.083700
20 CD19+CD268+ Survival Time 1.53e-03 0.084000
21 CD19+CD5+CD11b+ VAD.Indication Time 1.58e-03 0.084800
22 IL-5 LowIntermacs LowIntermacs 1.59e-03 0.084900
23 TNF-a VAD.Indication VAD.Indication 1.59e-03 0.085000
24 CD27-IgD+ mature naive AgeGreater60 Time 1.67e-03 0.086000
25 G-CSF Survival Time 1.84e-03 0.091300

Figures

# long
df.long <- melt(df.HMII, id.vars = colnames(df)[1:13])
names(df.long) <- make.names(names(df.long))

# list of identifiers for significant results to plot
unique_results <- unique(resulttable.sort[,c("biomarker","group")])

# make all timeseries plots
plots.ts <- mclapply(1:nrow(unique_results), function(ii){
    this_var <- as.character(unique_results[ii, "biomarker"])
    this_groups <- as.character(unique_results[ii, "group"])
    this_df.0 <- droplevels(subset(df.long, df.long$variable == this_var))
    this_df <- droplevels(this_df.0[!is.na(this_df.0$value),])
    ggplot(this_df) +
        aes(x = Time, y = value, group = PatientID) +
        aes_string(color = this_groups, fill = this_groups) +
        geom_line(alpha = 0.2) + 
        geom_point(alpha = 0.1) + 
        stat_summary(fun.y = mean, 
                     aes_string(group = this_groups), 
                     geom=c("point"), 
                     position = position_dodge(.5)) + 
        stat_summary(fun.y = mean, 
                     aes_string(group = this_groups), 
                     geom=c("line"), 
                     size = 2, 
                     position = position_dodge(.5)) + 
        # stat_smooth(method = "loess", 
        #             aes_string(group = this_groups), 
        #             size = .01, 
        #             span = 1, 
        #             alpha = 0.1) + 
        stat_sum_df(function(x) mean_cl_normal(x, conf.int = 0.68), 
                    mapping = aes_string(group = this_groups), 
                    position = position_dodge(.5)) + 
        scale_color_aaas() + scale_fill_aaas() +
        xlab("Time (days after surgery)") +
        ylab(this_var) + 
        scale_x_continuous(breaks = unique(this_df$Time)) +
        ggtitle(paste(this_var)) +
        theme_classic()
}, mc.cores = detectCores()-1)
names(plots.ts) <- paste(unique_results$biomarker, unique_results$group, sep = " | ")

p2stars <- function(p){
    if(p >= 0.1) s <- ""
    if(p < 0.1 & p >= 0.05) s <- "."
    if(p < 0.05 & p >= 0.01) s <- "*"
    if(p < 0.01 & p >= 0.001) s <- "**"
    if(p < 0.001 & p >= 0.0001) s <- "***"
    if(p < 0.0001 & p >= 0) s <- "****"
    return(s)
}

# print out results
for(ii in 1:length(plots.ts)){
    cat("  \n###", names(plots.ts)[ii], "\n")
    
    
    testid <- unique(models.fdr$testid[models.fdr$variable == unique_results$biomarker[ii] & 
                                           models.fdr$group == unique_results$group[ii]])
    this_anova <- anovatable.fdr[[testid]][,c("variable", "group", "parameter", "Chisq", "Df", "Pr(>Chisq)", "qval", "lfdr")]
    this_anova$stars <- sapply(this_anova$`Pr(>Chisq)`, p2stars)
    print(this_anova %>%
    # mutate(
    #     pvalue = cell_spec(pvalue, color = ifelse(resulttable.sort$pvalue > pcutoff, "grey", "green")),
    #     qvalue = cell_spec(qvalue, color = ifelse(resulttable.sort$qvalue > FDRcutoff, "grey", "green"))
    # ) %>% 
    kable(escape = F, row.names = F) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%"))
    cat("  \n")
    
    suppressWarnings(print(plots.ts[[ii]]))
    cat("  \n")
    
    model.summary <- summary(models.HMII[[testid]]$model)
    this_summary <- as.data.frame(model.summary$coefficients)
    this_summary$stars <- sapply(this_summary$`Pr(>|t|)`, p2stars)
    print(this_summary %>%
    # mutate(
    #     pvalue = cell_spec(pvalue, color = ifelse(resulttable.sort$pvalue > pcutoff, "grey", "green")),
    #     qvalue = cell_spec(qvalue, color = ifelse(resulttable.sort$qvalue > FDRcutoff, "grey", "green"))
    # ) %>% 
    kable(escape = F, row.names = T) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%"))
    cat("  \n")

}

MCP-1 | VAD.Indication

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
MCP-1 VAD.Indication VAD.Indication 0.7665451 1 0.3812880 0.7363448 0.9789241
MCP-1 VAD.Indication Time 36.1407299 4 0.0000003 0.0001117 0.0004958 ****
MCP-1 VAD.Indication VAD.Indication:Time 37.5815741 4 0.0000001 0.0001117 0.0001117 ****

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 561.90280 130.8896 39.03944 4.2929520 0.0001128 ***
VAD.Indication1 -114.59720 130.8896 39.03944 -0.8755256 0.3866432
Time1 764.73275 149.9178 49.76713 5.1010123 0.0000053 ****
Time3 22.58061 140.0921 49.14691 0.1611841 0.8726092
Time5 26.81099 141.2744 49.28012 0.1897796 0.8502614
Time8 62.45787 140.0921 49.14691 0.4458345 0.6576748
VAD.Indication1:Time1 -698.15698 149.9178 49.76713 -4.6569307 0.0000242 ****
VAD.Indication1:Time3 33.83061 140.0921 49.14691 0.2414884 0.8101813
VAD.Indication1:Time5 64.06099 141.2744 49.28012 0.4534509 0.6522146
VAD.Indication1:Time8 100.70787 140.0921 49.14691 0.7188692 0.4756257

G-CSF | RVAD

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
G-CSF RVAD RVAD 0.9711906 1 0.3243834 0.7103671 0.8927675
G-CSF RVAD Time 36.8079880 4 0.0000002 0.0001117 0.0001117 ****
G-CSF RVAD RVAD:Time 23.4918035 4 0.0001010 0.0178547 0.0655772 ***

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 80.858529 29.05715 43.64313 2.7827415 0.0079337 **
RVAD1 28.635529 29.05715 43.64313 0.9854900 0.3298156
Time1 154.373489 33.17328 49.28409 4.6535487 0.0000249 ****
Time3 -8.260040 32.98754 49.17215 -0.2503988 0.8033222
Time5 -12.489285 33.17328 49.28409 -0.3764863 0.7081715
Time8 1.965234 32.98754 49.17215 0.0595750 0.9527356
RVAD1:Time1 -137.156844 33.17328 49.28409 -4.1345574 0.0001379 ***
RVAD1:Time3 -25.361374 32.98754 49.17215 -0.7688168 0.4456792
RVAD1:Time5 -6.657618 33.17328 49.28409 -0.2006922 0.8417652
RVAD1:Time8 -13.601766 32.98754 49.17215 -0.4123305 0.6818894

CD27+IgD-IgM+ switched memory | Sensitized

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD27+IgD-IgM+ switched memory Sensitized Sensitized 1.636678 1 0.2007818 0.6480377 0.7942120
CD27+IgD-IgM+ switched memory Sensitized Time 39.142294 6 0.0000007 0.0002077 0.0118223 ****
CD27+IgD-IgM+ switched memory Sensitized Sensitized:Time 20.986487 5 0.0008148 0.0672896 0.1170453 ***

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 27.381872 4.119248 17.00582 6.6472993 0.0000041 ****
Sensitized1 5.392050 4.214754 18.12863 1.2793273 0.2169144
Time1 -12.864273 2.909664 23.34557 -4.4212228 0.0001912 ***
Time2 -5.898282 2.947423 23.34374 -2.0011658 0.0571320 .
Time3 1.110251 3.067201 23.49442 0.3619754 0.7206058
Time4 42.955373 14.695027 23.50196 2.9231231 0.0075435 **
Time5 -8.437023 2.909664 23.34557 -2.8996554 0.0080027 **
Time6 -10.689470 3.361716 23.31104 -3.1797657 0.0041298 **
Sensitized1:Time1 -5.551951 3.189779 23.38442 -1.7405443 0.0949085 .
Sensitized1:Time2 -7.529929 3.328970 23.46673 -2.2619395 0.0332577
Sensitized1:Time3 -14.534890 3.435471 23.57930 -4.2308282 0.0003030 ***
Sensitized1:Time4 40.519843 14.390728 23.59482 2.8156911 0.0096665 **
Sensitized1:Time5 -5.877201 3.189779 23.38442 -1.8425106 0.0781161 .

num lymph | AgeGreater60

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
num lymph AgeGreater60 AgeGreater60 19.46946 1 0.0000102 0.0025306 0.0561648 ****
num lymph AgeGreater60 Time 11.87890 6 0.0647256 0.5054532 0.7021147 .
num lymph AgeGreater60 AgeGreater60:Time 16.81779 6 0.0099767 0.2453627 0.4469460 **

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 98232.962 9565.859 16.93767 10.2691208 0.0000000 ****
AgeGreater601 42208.600 9565.859 16.93767 4.4124212 0.0003840 ***
Time1 -17358.438 17379.896 8030.90546 -0.9987653 0.3179385
Time2 -46436.374 19371.964 1017.84823 -2.3970917 0.0167053
Time3 -24345.933 18428.243 2543.63939 -1.3211207 0.1865799
Time4 4546.455 22578.518 2116.30122 0.2013620 0.8404349
Time5 -5057.389 16846.728 7767.08477 -0.3002001 0.7640326
Time6 43219.041 26820.842 1436.94476 1.6113976 0.1073127
AgeGreater601:Time1 -46178.068 17379.896 8030.90546 -2.6569819 0.0079000 **
AgeGreater601:Time2 -34749.595 19371.964 1017.84823 -1.7938086 0.0731405 .
AgeGreater601:Time3 -24691.948 18428.243 2543.63939 -1.3398970 0.1803985
AgeGreater601:Time4 22264.777 22578.518 2116.30122 0.9861044 0.3241946
AgeGreater601:Time5 1066.598 16846.728 7767.08477 0.0633119 0.9495198
AgeGreater601:Time6 21458.523 26820.842 1436.94476 0.8000689 0.4238031

IL-8 | RVAD

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
IL-8 RVAD RVAD 0.1597045 1 0.6894287 0.8345596 1.0000000
IL-8 RVAD Time 23.5113244 4 0.0001001 0.0177453 0.0561648 ***
IL-8 RVAD RVAD:Time 14.4619692 4 0.0059577 0.1881234 0.4469460 **

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 41.031296 15.91795 42.93629 2.5776752 0.0134632
RVAD1 6.361296 15.91795 42.93629 0.3996304 0.6914098
Time1 63.959123 18.28593 48.17847 3.4977230 0.0010196 **
Time3 18.776102 18.18381 48.05827 1.0325726 0.3069724
Time5 5.550902 18.28593 48.17847 0.3035613 0.7627692
Time8 63.811279 18.18381 48.05827 3.5092363 0.0009868 ***
RVAD1:Time1 -26.077543 18.28593 48.17847 -1.4260987 0.1602877
RVAD1:Time3 -17.753898 18.18381 48.05827 -0.9763576 0.3337766
RVAD1:Time5 -2.605765 18.28593 48.17847 -0.1425011 0.8872785
RVAD1:Time8 -60.798721 18.18381 48.05827 -3.3435638 0.0016095 **

IP-10 | VAD.Indication

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
IP-10 VAD.Indication VAD.Indication 2.796429 1 0.0944745 0.5543465 0.7021147 .
IP-10 VAD.Indication Time 1.654803 4 0.7989103 0.8539194 1.0000000
IP-10 VAD.Indication VAD.Indication:Time 22.574963 4 0.0001539 0.0238200 0.1170453 ***

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 863.28009 136.6240 38.36077 6.3186544 0.0000002 ****
VAD.Indication1 -228.46991 136.6240 38.36077 -1.6722527 0.1026129
Time1 -58.05381 156.0408 50.35251 -0.3720426 0.7114224
Time3 -177.14371 145.8087 49.71218 -1.2149047 0.2301415
Time5 -36.13792 147.0403 49.84989 -0.2457687 0.8068704
Time8 -67.64178 145.8087 49.71218 -0.4639076 0.6447390
VAD.Indication1:Time1 -72.29918 156.0408 50.35251 -0.4633352 0.6451207
VAD.Indication1:Time3 361.85629 145.8087 49.71218 2.4817190 0.0165007
VAD.Indication1:Time5 499.61208 147.0403 49.84989 3.3977891 0.0013430 **
VAD.Indication1:Time8 403.10822 145.8087 49.71218 2.7646371 0.0079736 **

CD19+CD268+ | RVAD

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19+CD268+ RVAD RVAD 0.0657854 1 0.7975751 0.8537106 1.0000000
CD19+CD268+ RVAD Time 24.0929914 6 0.0005021 0.0532019 0.1170453 ***
CD19+CD268+ RVAD RVAD:Time 12.4104462 5 0.0295766 0.3899639 0.5885059

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 71.0915597 6.669442 18.59108 10.6592966 0.0000000 ****
RVAD1 1.7084904 6.661127 18.49977 0.2564867 0.8004057
Time1 7.2862388 3.287694 55.49353 2.2162156 0.0307947
Time2 12.1505338 3.370183 55.42071 3.6053042 0.0006688 ***
Time3 -2.0649129 3.603627 56.71934 -0.5730097 0.5689030
Time4 0.6284739 5.288882 56.31204 0.1188293 0.9058335
Time5 -5.5692341 3.034159 55.63506 -1.8355118 0.0717768 .
Time6 -7.1847165 3.710915 55.33508 -1.9361038 0.0579695 .
RVAD1:Time1 -5.8129680 3.247756 55.52207 -1.7898414 0.0789332 .
RVAD1:Time2 -6.2774091 3.315041 55.43531 -1.8936141 0.0634995 .
RVAD1:Time3 8.1847488 3.546151 56.73923 2.3080656 0.0246632
RVAD1:Time4 -0.4086623 5.254895 56.33024 -0.0777679 0.9382883
RVAD1:Time5 4.3552802 2.981250 55.63204 1.4608907 0.1496707

CD268 of +27-38++transitional | AgeGreater60

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD268 of +27-38++transitional AgeGreater60 AgeGreater60 0.2322852 1 0.6298345 0.8216969 1.0000000
CD268 of +27-38++transitional AgeGreater60 Time 13.6403081 6 0.0339226 0.4075803 0.5885059
CD268 of +27-38++transitional AgeGreater60 AgeGreater60:Time 23.6053408 6 0.0006171 0.0592243 0.1170453 ***

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 42.8890027 5.456738 18.62523 7.8598238 0.0000002 ****
AgeGreater601 -2.6299282 5.456738 18.62523 -0.4819597 0.6354494
Time1 7.7769537 3.661163 54.86739 2.1241759 0.0381774
Time2 11.1890385 4.235025 55.98436 2.6420240 0.0106649
Time3 -0.5785914 3.960824 55.66291 -0.1460786 0.8843871
Time4 -0.4458228 4.848810 55.41628 -0.0919448 0.9270735
Time5 -3.0182156 3.552198 54.93024 -0.8496754 0.3991937
Time6 -11.5596086 5.795202 55.52990 -1.9946861 0.0509946 .
AgeGreater601:Time1 -3.4675113 3.661163 54.86739 -0.9471066 0.3477372
AgeGreater601:Time2 -2.7610575 4.235025 55.98436 -0.6519577 0.5170962
AgeGreater601:Time3 -11.1940766 3.960824 55.66291 -2.8261991 0.0065302 **
AgeGreater601:Time4 9.2911740 4.848810 55.41628 1.9161760 0.0605064 .
AgeGreater601:Time5 10.1433722 3.552198 54.93024 2.8555198 0.0060524 **
AgeGreater601:Time6 -9.0603563 5.795202 55.52990 -1.5634238 0.1236345

GRO | Sensitized

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
GRO Sensitized Sensitized 1.498174 1 0.2209526 0.6591120 0.7942120
GRO Sensitized Time 19.222649 4 0.0007106 0.0633400 0.1170453 ***
GRO Sensitized Sensitized:Time 2.295863 4 0.6815224 0.8329609 1.0000000

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 428.46846 74.37223 14.44520 5.7611351 0.0000436 ****
Sensitized1 91.03154 74.37223 14.44520 1.2239991 0.2405420
Time1 -122.47863 75.67331 18.12125 -1.6185182 0.1228240
Time3 -110.10890 75.54944 18.06193 -1.4574417 0.1621621
Time5 12.21766 79.17311 18.29747 0.1543157 0.8790502
Time8 166.57488 75.94501 18.14856 2.1933617 0.0415419
Sensitized1:Time1 -93.54170 75.67331 18.12125 -1.2361255 0.2321958
Sensitized1:Time3 -58.67197 75.54944 18.06193 -0.7766037 0.4474442
Sensitized1:Time5 -55.23799 79.17311 18.29747 -0.6976862 0.4941446
Sensitized1:Time8 -103.32488 75.94501 18.14856 -1.3605223 0.1903239

CD19 of live lymph | Sex

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19 of live lymph Sex Sex 0.1225005 1 0.7263382 0.8416352 1.0000000
CD19 of live lymph Sex Time 23.0557835 6 0.0007780 0.0659624 0.1170453 ***
CD19 of live lymph Sex Sex:Time 5.1871659 6 0.5200417 0.7918872 1.0000000

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 12.5892985 1.879525 18.27074 6.6981285 0.0000026 ****
Sex1 0.6578350 1.879525 18.27074 0.3500007 0.7303405
Time1 -1.0752514 2.031785 55.49745 -0.5292153 0.5987667
Time2 9.2449391 2.277440 57.75730 4.0593551 0.0001498 ***
Time3 4.2165338 2.237468 58.17325 1.8845112 0.0644952 .
Time4 -0.9765939 2.876371 58.99847 -0.3395230 0.7354215
Time5 -1.9466931 2.148457 57.48071 -0.9060890 0.3686714
Time6 -3.3473554 3.026480 58.97719 -1.1060227 0.2732080
Sex1:Time1 0.1741180 2.031785 55.49745 0.0856971 0.9320156
Sex1:Time2 4.3823140 2.277440 57.75730 1.9242278 0.0592577 .
Sex1:Time3 -0.2634753 2.237468 58.17325 -0.1177560 0.9066666
Sex1:Time4 0.9753035 2.876371 58.99847 0.3390743 0.7357577
Sex1:Time5 -0.7827484 2.148457 57.48071 -0.3643305 0.7169481
Sex1:Time6 -0.1889350 3.026480 58.97719 -0.0624273 0.9504336

CD19+27+IgD-38++IgG ASC | RVAD

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19+27+IgD-38++IgG ASC RVAD RVAD 1.488335 1 0.2224750 0.6598801 0.7942120
CD19+27+IgD-38++IgG ASC RVAD Time 22.817493 6 0.0008600 0.0688251 0.1170453 ***
CD19+27+IgD-38++IgG ASC RVAD RVAD:Time 17.101744 5 0.0043108 0.1546736 0.3213720 **

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 2.6203538 0.4818057 25.08410 5.4386107 0.0000119 ****
RVAD1 -0.5820248 0.4770799 24.32938 -1.2199734 0.2341693
Time1 -0.4829397 0.7950820 60.53341 -0.6074087 0.5458516
Time2 -0.3623417 0.8101593 60.04771 -0.4472475 0.6563056
Time3 -0.9487047 0.7526078 69.94667 -1.2605566 0.2116575
Time4 4.9272893 1.0629443 63.91519 4.6355104 0.0000181 ****
Time5 -1.1542426 0.6975396 60.29710 -1.6547341 0.1031751
Time6 -1.0123485 0.7731201 59.04495 -1.3094324 0.1954605
RVAD1:Time1 1.6503809 0.7872629 60.72805 2.0963530 0.0402249
RVAD1:Time2 0.7059977 0.8000041 60.09863 0.8824926 0.3810268
RVAD1:Time3 0.5263792 0.7415354 69.96874 0.7098503 0.4801560
RVAD1:Time4 -4.1269689 1.0552262 64.05477 -3.9109802 0.0002248 ***
RVAD1:Time5 0.7750050 0.6879253 60.22781 1.1265830 0.2643876

TNF-a | VAD.Indication

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
TNF-a VAD.Indication VAD.Indication 9.965749 1 0.0015948 0.084951 0.1170453 **
TNF-a VAD.Indication Time 10.875544 4 0.0279989 0.382689 0.5885059
TNF-a VAD.Indication VAD.Indication:Time 18.791729 4 0.0008636 0.068943 0.1170453 ***

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 46.39425 6.242760 46.96050 7.431689 0.0000000 ****
VAD.Indication1 -19.70750 6.242760 46.96050 -3.156857 0.0027848 **
Time1 -15.97599 7.777895 50.58175 -2.054025 0.0451592
Time3 -20.73240 7.277482 49.73388 -2.848843 0.0063659 **
Time5 -19.13826 7.336929 49.90668 -2.608484 0.0119672
Time8 -18.60823 7.277482 49.73388 -2.556960 0.0136582
VAD.Indication1:Time1 13.61045 7.777895 50.58175 1.749888 0.0862022 .
VAD.Indication1:Time3 26.04310 7.277482 49.73388 3.578586 0.0007821 ***
VAD.Indication1:Time5 24.12499 7.336929 49.90668 3.288159 0.0018522 **
VAD.Indication1:Time8 25.57552 7.277482 49.73388 3.514337 0.0009497 ***

sCD40L | Sensitized

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
sCD40L Sensitized Sensitized 3.144392 1 0.0761884 0.5276911 0.7021147 .
sCD40L Sensitized Time 18.617338 4 0.0009343 0.0711582 0.1170453 ***
sCD40L Sensitized Sensitized:Time 4.966288 4 0.2907741 0.6939787 0.8927675

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 5143.6942 1093.226 13.98188 4.7050599 0.0003391 ***
Sensitized1 1938.5558 1093.226 13.98188 1.7732433 0.0979627 .
Time1 -2404.0569 1042.935 40.71754 -2.3050879 0.0263347
Time3 -2778.8665 1041.062 40.60975 -2.6692624 0.0108767
Time5 -1369.0943 1091.673 41.01647 -1.2541252 0.2168970
Time8 953.1374 1046.749 40.75782 0.9105689 0.3678765
Sensitized1:Time1 -1894.9185 1042.935 40.71754 -1.8169094 0.0765966 .
Sensitized1:Time3 -2038.4781 1041.062 40.60975 -1.9580764 0.0571155 .
Sensitized1:Time5 -1399.5477 1091.673 41.01647 -1.2820213 0.2070344
Sensitized1:Time8 -1679.8874 1046.749 40.75782 -1.6048612 0.1162461

IL-1b | LowIntermacs

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
IL-1b LowIntermacs LowIntermacs 4.882947 1 0.0271233 0.3784164 0.5885059
IL-1b LowIntermacs Time 18.060820 4 0.0012008 0.0779384 0.1170453 **
IL-1b LowIntermacs LowIntermacs:Time 10.387189 4 0.0343869 0.4092792 0.6622384

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 3.9300857 1.224314 37.85729 3.2100312 0.0027049 **
LowIntermacs1 -2.7054143 1.224314 37.85729 -2.2097392 0.0332453
Time1 -1.8535475 1.295245 48.08715 -1.4310398 0.1588858
Time3 -0.9004147 1.309640 48.36199 -0.6875283 0.4950341
Time5 -0.3996779 1.295245 48.08715 -0.3085731 0.7589791
Time8 3.6212519 1.352478 47.76025 2.6774934 0.0101373
LowIntermacs1:Time1 2.2003761 1.295245 48.08715 1.6988105 0.0958169 .
LowIntermacs1:Time3 2.0036853 1.309640 48.36199 1.5299510 0.1325442
LowIntermacs1:Time5 1.6665065 1.295245 48.08715 1.2866340 0.2043817
LowIntermacs1:Time8 4.3209519 1.352478 47.76025 3.1948399 0.0024795 **

CD19+CD268+ | AgeGreater60

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19+CD268+ AgeGreater60 AgeGreater60 0.3469473 1 0.5558463 0.8026471 1.0000000
CD19+CD268+ AgeGreater60 Time 21.6351602 6 0.0014096 0.0819979 0.1170453 **
CD19+CD268+ AgeGreater60 AgeGreater60:Time 13.2451783 6 0.0393036 0.4298171 0.6622384

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 70.4017575 5.672437 18.15725 12.4112019 0.0000000 ****
AgeGreater601 -3.3411917 5.672437 18.15725 -0.5890223 0.5631035
Time1 4.9765953 2.590762 54.25758 1.9209001 0.0600052 .
Time2 10.1512843 3.005294 54.77171 3.3778010 0.0013511 **
Time3 3.2224413 2.808601 54.64585 1.1473474 0.2562366
Time4 0.7309697 3.435885 54.50554 0.2127457 0.8323190
Time5 -1.9134713 2.514085 54.29129 -0.7611005 0.4498903
Time6 -11.8954405 4.107626 54.55324 -2.8959406 0.0054286 **
AgeGreater601:Time1 6.6645644 2.590762 54.25758 2.5724339 0.0128675
AgeGreater601:Time2 2.9239069 3.005294 54.77171 0.9729188 0.3348709
AgeGreater601:Time3 -2.9963497 2.808601 54.64585 -1.0668477 0.2907313
AgeGreater601:Time4 2.5287625 3.435885 54.50554 0.7359857 0.4648951
AgeGreater601:Time5 1.6646309 2.514085 54.29129 0.6621220 0.5106939
AgeGreater601:Time6 -10.5021121 4.107626 54.55324 -2.5567353 0.0133799

CD19+27+IgD-38++IgG ASC | LowIntermacs

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19+27+IgD-38++IgG ASC LowIntermacs LowIntermacs 10.064934 1 0.0015112 0.0836814 0.1170453 **
CD19+27+IgD-38++IgG ASC LowIntermacs Time 7.025492 6 0.3184962 0.7076945 0.8927675
CD19+27+IgD-38++IgG ASC LowIntermacs LowIntermacs:Time 4.762124 6 0.5746644 0.8078680 1.0000000

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 2.0811760 0.2973039 69 7.0001639 0.0000000 ****
LowIntermacs1 -0.9432049 0.2973039 69 -3.1725279 0.0022566 **
Time1 0.5846573 0.6171169 69 0.9474012 0.3467406
Time2 -0.0721760 0.6703347 69 -0.1076716 0.9145687
Time3 -0.5655510 0.6288525 69 -0.8993381 0.3716005
Time4 1.5725740 0.7259856 69 2.1661228 0.0337595
Time5 -0.5928189 0.5865739 69 -1.0106465 0.3157166
Time6 -0.5021760 0.9084865 69 -0.5527611 0.5822134
LowIntermacs1:Time1 -0.6042951 0.6171169 69 -0.9792230 0.3308907
LowIntermacs1:Time2 -0.8877951 0.6703347 69 -1.3244056 0.1897386
LowIntermacs1:Time3 0.1738299 0.6288525 69 0.2764240 0.7830487
LowIntermacs1:Time4 -0.6630451 0.7259856 69 -0.9133033 0.3642633
LowIntermacs1:Time5 0.2905621 0.5865739 69 0.4953546 0.6219238
LowIntermacs1:Time6 0.9742049 0.9084865 69 1.0723384 0.2873043

CD19+CD268+ | Survival

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19+CD268+ Survival Survival 0.5237235 1 0.4692576 0.7744448 1.0000000
CD19+CD268+ Survival Time 21.4396868 6 0.0015290 0.0839597 0.1170453 **
CD19+CD268+ Survival Survival:Time 12.0549575 5 0.0340424 0.4080218 0.5885059

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 77.3857047 8.550443 50.29588 9.0504906 0.0000000 ****
Survival1 -6.2297081 8.608285 51.05594 -0.7236874 0.4725605
Time1 1.2586721 6.558233 55.54771 0.1919225 0.8485028
Time2 6.8753969 7.022070 55.85850 0.9791125 0.3317461
Time3 -3.5152345 6.503761 55.63135 -0.5404926 0.5910148
Time4 -5.0732303 6.666546 55.57442 -0.7609983 0.4498751
Time5 -10.1390779 6.423775 55.64638 -1.5783676 0.1201496
Time6 -8.5954800 4.015982 55.47965 -2.1403184 0.0367401
Survival1:Time1 0.1192086 6.703563 55.50917 0.0177829 0.9858758
Survival1:Time2 -0.1157145 7.192986 55.82665 -0.0160871 0.9872222
Survival1:Time3 9.5012717 6.746371 55.68241 1.4083530 0.1645858
Survival1:Time4 3.6537040 6.873994 55.48443 0.5315256 0.5971763
Survival1:Time5 10.9846681 6.603540 55.64891 1.6634514 0.1018445

CD19+CD5+CD11b+ | VAD.Indication

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD19+CD5+CD11b+ VAD.Indication VAD.Indication 1.91482 1 0.1664289 0.6243197 0.7942120
CD19+CD5+CD11b+ VAD.Indication Time 21.35859 6 0.0015813 0.0847528 0.1170453 **
CD19+CD5+CD11b+ VAD.Indication VAD.Indication:Time 16.83455 6 0.0099109 0.2446299 0.4469460 **

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 7.9968359 2.110445 18.75340 3.7891699 0.0012641 **
VAD.Indication1 -2.9203711 2.110445 18.75340 -1.3837701 0.1826791
Time1 -2.9840791 1.437235 55.44344 -2.0762634 0.0425182
Time2 -3.7424834 1.774888 56.60214 -2.1085745 0.0394194
Time3 -2.6176766 1.571916 55.86312 -1.6652772 0.1014567
Time4 -0.8754918 1.695334 55.98904 -0.5164125 0.6076001
Time5 3.2240295 1.402625 55.24299 2.2985690 0.0253372
Time6 1.0255516 1.977519 56.03007 0.5186052 0.6060783
VAD.Indication1:Time1 3.3106546 1.437235 55.44344 2.3034882 0.0250253
VAD.Indication1:Time2 0.3952206 1.774888 56.60214 0.2226735 0.8245912
VAD.Indication1:Time3 2.6404233 1.571916 55.86312 1.6797478 0.0985913 .
VAD.Indication1:Time4 2.2117152 1.695334 55.98904 1.3045894 0.1973699
VAD.Indication1:Time5 -1.8530160 1.402625 55.24299 -1.3211061 0.1919121
VAD.Indication1:Time6 -0.7114396 1.977519 56.03007 -0.3597637 0.7203763

IL-5 | LowIntermacs

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
IL-5 LowIntermacs LowIntermacs 9.968715 1 0.0015922 0.0849134 0.1170453 **
IL-5 LowIntermacs Time 5.213550 4 0.2660793 0.6811998 0.8438190
IL-5 LowIntermacs LowIntermacs:Time 11.797739 4 0.0189205 0.3276987 0.5885059

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 10.249174 2.270314 50.69030 4.514430 0.0000380 ****
LowIntermacs1 -7.168126 2.270314 50.69030 -3.157327 0.0026812 **
Time1 -6.021193 2.720645 49.36927 -2.213149 0.0315398
Time3 -4.095569 2.747092 49.66254 -1.490874 0.1423193
Time5 -3.732713 2.720645 49.36927 -1.371995 0.1762674
Time8 -4.198132 2.747092 49.66254 -1.528209 0.1328054
LowIntermacs1:Time1 6.241144 2.720645 49.36927 2.293994 0.0260839
LowIntermacs1:Time3 7.862230 2.747092 49.66254 2.862019 0.0061466 **
LowIntermacs1:Time5 8.177498 2.720645 49.36927 3.005720 0.0041567 **
LowIntermacs1:Time8 6.540468 2.747092 49.66254 2.380870 0.0211494

CD27-IgD+ mature naive | AgeGreater60

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
CD27-IgD+ mature naive AgeGreater60 AgeGreater60 0.1101225 1 0.7400047 0.8441039 1.0000000
CD27-IgD+ mature naive AgeGreater60 Time 21.2315568 6 0.0016669 0.0859702 0.2201208 **
CD27-IgD+ mature naive AgeGreater60 AgeGreater60:Time 16.1873437 6 0.0127830 0.2723265 0.4469460

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 46.5140046 3.819353 18.37019 12.1785044 0.0000000 ****
AgeGreater601 -1.2674413 3.819353 18.37019 -0.3318471 0.7437609
Time1 4.1049996 1.882829 54.48557 2.1802295 0.0335785
Time2 7.6098682 2.183223 55.08195 3.4856114 0.0009714 ***
Time3 -2.1372913 2.040535 54.93213 -1.0474172 0.2994941
Time4 -0.6836866 2.496542 54.77401 -0.2738534 0.7852274
Time5 -2.5629872 1.827057 54.52383 -1.4027953 0.1663467
Time6 -6.4910038 2.984524 54.83008 -2.1748876 0.0339697
AgeGreater601:Time1 0.6398920 1.882829 54.48557 0.3398567 0.7352712
AgeGreater601:Time2 1.5157353 2.183223 55.08195 0.6942649 0.4904341
AgeGreater601:Time3 -0.9355877 2.040535 54.93213 -0.4585012 0.6484013
AgeGreater601:Time4 3.5789463 2.496542 54.77401 1.4335613 0.1573814
AgeGreater601:Time5 5.4493073 1.827057 54.52383 2.9825598 0.0042680 **
AgeGreater601:Time6 -8.1113742 2.984524 54.83008 -2.7178119 0.0087786 **

G-CSF | Survival

variable group parameter Chisq Df Pr(>Chisq) qval lfdr stars
G-CSF Survival Survival 0.7629021 1 0.3824218 0.7368862 0.9789241
G-CSF Survival Time 17.1044346 4 0.0018447 0.0913362 0.3213720 **
G-CSF Survival Survival:Time 6.3623959 4 0.1736697 0.6299380 0.7942120

Estimate Std. Error df t value Pr(>|t|) stars
(Intercept) 90.258056 26.49728 50.55062 3.4063138 0.0012987 **
Survival1 23.143856 26.49728 50.55062 0.8734427 0.3865523
Time1 95.399454 32.42302 49.69739 2.9423373 0.0049380 **
Time3 -18.500920 32.06615 49.42145 -0.5769610 0.5665848
Time5 -15.312156 32.42302 49.69739 -0.4722619 0.6388071
Time8 -6.183344 32.06615 49.42145 -0.1928309 0.8478817
Survival1:Time1 -68.906746 32.42302 49.69739 -2.1252416 0.0385612
Survival1:Time3 -15.419320 32.06615 49.42145 -0.4808597 0.6327383
Survival1:Time5 -6.513356 32.42302 49.69739 -0.2008868 0.8416070
Survival1:Time8 0.071856 32.06615 49.42145 0.0022409 0.9982211